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Model: EphAsad/Atem-v1-1.5B Source: Original Platform
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FROM Atem-1.5b.Q4_K_M.gguf
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TEMPLATE """{{- if .Messages }}
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{{- if or .System .Tools }}<|im_start|>system
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{{- if .System }}
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{{ .System }}
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{{- end }}
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{{- if .Tools }}
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# Tools
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||||||
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You may call one or more functions to assist with the user query.
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You are provided with function signatures within <tools></tools> XML tags:
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<tools>
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{{- range .Tools }}
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{"type": "function", "function": {{ .Function }}}
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{{- end }}
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</tools>
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For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
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<tool_call>
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{"name": <function-name>, "arguments": <args-json-object>}
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</tool_call>
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{{- end }}<|im_end|>
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{{ end }}
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{{- range $i, $_ := .Messages }}
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{{- $last := eq (len (slice $.Messages $i)) 1 -}}
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{{- if eq .Role "user" }}<|im_start|>user
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{{ .Content }}<|im_end|>
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{{ else if eq .Role "assistant" }}<|im_start|>assistant
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{{ if .Content }}{{ .Content }}
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{{- else if .ToolCalls }}<tool_call>
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{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
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{{ end }}</tool_call>
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{{- end }}{{ if not $last }}<|im_end|>
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{{ end }}
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{{- else if eq .Role "tool" }}<|im_start|>user
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<tool_response>
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{{ .Content }}
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</tool_response><|im_end|>
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{{ end }}
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{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
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{{ end }}
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{{- end }}
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{{- else }}
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{{- if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ if .Prompt }}<|im_start|>user
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{{ .Prompt }}<|im_end|>
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{{ end }}<|im_start|>assistant
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{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
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PARAMETER stop "<|im_end|>"
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PARAMETER stop "<|endoftext|>"
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PARAMETER temperature 0.7
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PARAMETER min_p 0.1
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SYSTEM """You are Atem, a precise and analytical reasoning assistant. You approach every problem methodically — identifying core concepts, reasoning step by step, and arriving at well-supported conclusions. You show your thinking clearly and are thorough, direct, and intellectually honest."""
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---
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language:
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- en
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license: apache-2.0
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base_model:
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- Qwen/Qwen2.5-1.5B-Instruct
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tags:
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- text-generation
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- qwen2
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- unsloth
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- lora
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- gguf
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- llama.cpp
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- reasoning
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- distillation
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- conversational
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pipeline_tag: text-generation
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library_name: transformers
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datasets:
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- EphAsad/QWENMillenium-SF
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- EphAsad/Phi4Millennium-SF
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- EphAsad/MistralMillenium-SF
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- Modotte/CodeX-2M-Thinking
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- Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned
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||||||
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- WithinUsAI/MiniMax_M2.7_Distilled_5k
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- tuanha1305/DeepSeek-R1-Distill
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- open-r1/OpenThoughts-114k-math
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- flytech/python-codes-25k
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- FreedomIntelligence/medical-o1-reasoning-SFT
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model-index:
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- name: Atem v1
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: ARC-Challenge
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type: ai2_arc
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config: ARC-Challenge
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split: test
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metrics:
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- type: acc_norm
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||||||
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value: 0.455
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name: Accuracy (normalised)
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verified: false
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8K
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type: gsm8k
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split: test
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metrics:
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- type: exact_match
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||||||
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value: 0.530
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||||||
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name: Exact Match (strict, zero-shot)
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verified: false
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||||||
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag
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type: hellaswag
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split: validation
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metrics:
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||||||
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- type: acc_norm
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||||||
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value: 0.644
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||||||
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name: Accuracy (normalised)
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verified: false
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||||||
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---
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||||||
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<p align="center">
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<img src="Logo.png" width="300" alt="Atem Logo"/>
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</p>
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<h1 align="center">Atem v1</h1>
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<p align="center">
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<em>Ancient logic. Modern intelligence.</em>
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</p>
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<p align="center">
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A 1.5B reasoning model trained via multi-source knowledge distillation from frontier teacher models.
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</p>
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<p align="center">
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<img src="https://img.shields.io/badge/Base-Qwen2.5--1.5B--Instruct-blue" alt="Base Model"/>
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<img src="https://img.shields.io/badge/Method-LoRA%20SFT-purple" alt="Method"/>
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<img src="https://img.shields.io/badge/Parameters-1.5B-orange" alt="Parameters"/>
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<img src="https://img.shields.io/badge/License-Apache%202.0-green" alt="License"/>
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</p>
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||||||
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---
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||||||
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## Overview
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Atem is a 1.5B parameter reasoning model built via supervised fine-tuning on a curated corpus of approximately 115,000 examples distilled from multiple frontier teacher models. Starting from Qwen2.5-1.5B-Instruct, Atem was trained using LoRA to preserve base model capabilities while improving performance on reasoning, mathematics, and coding tasks.
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This is **Stage 1** of a planned multi-stage training series. Stage 1 focuses on establishing strong general reasoning across domains. Stage 2 layers chain-of-thought thinking traces on top of this foundation.
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Stage 2 is [Atem-Wisdom](https://huggingface.co/EphAsad/Atem-Wisdom-1.5B) which builds on this foundation by adding explicit chain-of-thought reasoning — the model works through problems inside <think> tags before producing its final answer.
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---
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## Model Details
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| Property | Value |
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|----------|-------|
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| **Base model** | Qwen/Qwen2.5-1.5B-Instruct |
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| **Training method** | LoRA Supervised Fine-Tuning (Stage 1) |
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| **LoRA config** | r=32, alpha=64, dropout=0.05 |
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| **Target modules** | q, k, v, o, gate, up, down projections |
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| **Parameters** | ~1.54B |
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| **Training records** | ~114,932 |
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| **Epochs** | 1 |
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| **Effective batch size** | 64 (batch 8 × grad accum 8) |
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| **Learning rate** | 2e-4, cosine schedule, 5% warmup |
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| **Final train loss** | 0.940 |
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| **Final val loss** | 0.890 |
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| **Hardware** | NVIDIA A100-SXM4 80GB |
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| **Max sequence length** | 4,096 tokens |
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| **Precision** | bfloat16 |
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| **License** | Apache 2.0 |
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---
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## Intended Use
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Atem is designed for open-ended reasoning tasks where structured, accurate thinking adds value:
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- Code explanation, implementation, and debugging
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- Mathematical problem solving with working shown
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- Analytical reasoning and hypothesis evaluation
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- Concept explanation and comparative analysis
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- Logic, argument, and fallacy identification
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Atem is **not** designed for retrieval-heavy factual lookup, real-time information, or tasks requiring broad knowledge breadth beyond its training domains.
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---
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## Training Data
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Atem was trained on a corpus assembled from eleven sources, combining domain-specific generated datasets and publicly available distillation datasets from frontier models. All outputs containing `<think>` reasoning traces were stripped to clean final responses for Stage 1 training.
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| Dataset | Records | Source / Teacher |
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|---------|---------|-----------------|
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| EphAsad/QWENMillenium-SF | 5,000 | Qwen2.5-14B — Analytical & Scientific |
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| EphAsad/Phi4Millennium-SF | 2,932 | Phi-4 14B — Mathematical Reasoning |
|
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| EphAsad/MistralMillenium-SF | 5,000 | Mistral-Nemo-12B — Language & Comprehension |
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| Modotte/CodeX-2M-Thinking | 30,000 | Mixed — Coding |
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| Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned | 23,000 | Kimi K2.5 — General Distillation (English filtered) |
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| WithinUsAI/MiniMax_M2.7_Distilled_5k | 5,000 | MiniMax M2.7 |
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| tuanha1305/DeepSeek-R1-Distill | 9,000 | DeepSeek-R1 |
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| open-r1/OpenThoughts-114k-math | 10,000 | Mixed — Mathematics (correct answers only) |
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| flytech/python-codes-25k | 10,000 | Python coding |
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| FreedomIntelligence/medical-o1-reasoning-SFT | 10,000 | Medical reasoning (English config) |
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| Private dataset | 5,000 | Undisclosed |
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| **Total** | **~114,932** | |
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The QWENMillenium-SF, Phi4Millennium-SF, and MistralMillenium-SF datasets were generated specifically for this project via batched inference on Colab A100. OpenThoughts-114k-math was filtered to verified correct solutions only before sampling.
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||||||
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---
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## Training Configuration
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```python
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# Key hyperparameters
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lora_r = 32
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lora_alpha = 64
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lora_dropout = 0.05
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max_seq_length = 4096
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learning_rate = 2e-4
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lr_scheduler = 'cosine'
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warmup_ratio = 0.05
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batch_size = 8
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grad_accumulation = 8 # effective batch size: 64
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num_epochs = 1
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dtype = bfloat16
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load_in_4bit = True # during training
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```
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Training used Unsloth with `train_on_responses_only` masking, ensuring loss was computed exclusively on assistant response tokens. A three-part pre-training validation was run before training: chat template replacement verification, think tag strip confirmation, and mask sanity check.
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After training, LoRA adapters were merged into the base weights and exported as a full merged model.
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**Loss curve:**
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| Step | Train Loss | Val Loss |
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|------|-----------|----------|
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| 500 | 0.990 | 0.920 |
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| 1000 | 1.020 | 0.900 |
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| 1500 | 0.960 | 0.890 |
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| Final | **0.940** | **0.890** |
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Validation loss converged at 0.890, with a final train/val gap of 0.050 — indicating no overfitting over the single epoch.
|
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---
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## Evaluation
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||||||
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### Benchmark Results
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|
||||||
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Evaluated against Qwen2.5-1.5B-Instruct (base model) using lm-evaluation-harness with identical conditions: 4-bit inference, batch size 16, zero-shot strict evaluation.
|
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||||||
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| Task | Base (1.5B) | Atem v1 (1.5B) | Delta |
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|------|------------|----------------|-------|
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||||||
|
| ARC-Challenge | 43.7% | 45.5% | +1.8% ✓ |
|
||||||
|
| GSM8K | 23.0% | **53.0%** | **+30.0%** ✓ |
|
||||||
|
| HellaSwag | 66.8% | 64.4% | -2.4% |
|
||||||
|
|
||||||
|
The GSM8K result is the primary finding. A +30 percentage point improvement on grade school mathematics reflects the targeted training on verified correct mathematical reasoning examples from multiple frontier teacher models.
|
||||||
|
|
||||||
|
The HellaSwag regression of 2.4% is within normal benchmark variance and represents a significant improvement over a prior exploratory training run using full fine-tune, which produced a 16.2% regression on the same benchmark. LoRA preserved base model commonsense capabilities as intended.
|
||||||
|
|
||||||
|
### Comparison vs Qwen2.5-7B-Instruct
|
||||||
|
|
||||||
|
To contextualise the GSM8K result, Atem was benchmarked against Qwen2.5-7B-Instruct under the same zero-shot strict evaluation conditions.
|
||||||
|
|
||||||
|
| Model | Parameters | GSM8K (zero-shot strict) |
|
||||||
|
|-------|-----------|--------------------------|
|
||||||
|
| Qwen2.5-1.5B-Instruct | 1.5B | 23.0% |
|
||||||
|
| **Atem v1** | **1.5B** | **53.0%** |
|
||||||
|
| Qwen2.5-7B-Instruct | 7B | 74.9% |
|
||||||
|
|
||||||
|
At baseline, the 1.5B model sits 51.9 points below the 7B. After training, Atem sits 21.9 points below — closing approximately **58% of the capability gap** between 1.5B and 7B on mathematical reasoning. Atem achieves **71% of Qwen2.5-7B's GSM8K performance at 22% of its parameter count**.
|
||||||
|
|
||||||
|
Note: Official Qwen2.5-7B-Instruct scores (91.6% GSM8K) use 4-shot chain-of-thought prompting. The 74.9% figure above reflects the same zero-shot strict evaluation format used for Atem, ensuring a fair direct comparison.
|
||||||
|
|
||||||
|
### Qualitative Evaluation
|
||||||
|
|
||||||
|
Atem was evaluated against Qwen2.5-1.5B-Instruct across 30 domain-representative questions using matched system prompts, ensuring differences in output reflect trained capability rather than prompt engineering.
|
||||||
|
|
||||||
|
| Domain | Questions | Outcome |
|
||||||
|
|--------|-----------|---------|
|
||||||
|
| Coding | 8 | Atem stronger — more thorough, better structured, catches edge cases |
|
||||||
|
| Mathematics | 6 | Comparable — both accurate on standard problems |
|
||||||
|
| Analytical Reasoning | 6 | Atem stronger — better structured arguments |
|
||||||
|
| General Knowledge | 5 | Comparable |
|
||||||
|
| Language & Logic | 5 | Atem stronger — correct fallacy identification, greater depth |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Usage
|
||||||
|
|
||||||
|
### Transformers
|
||||||
|
|
||||||
|
```python
|
||||||
|
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||||
|
import torch
|
||||||
|
|
||||||
|
model_name = "EphAsad/Atem-v1-1.5B"
|
||||||
|
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||||
|
model = AutoModelForCausalLM.from_pretrained(
|
||||||
|
model_name,
|
||||||
|
torch_dtype=torch.bfloat16,
|
||||||
|
device_map="auto"
|
||||||
|
)
|
||||||
|
|
||||||
|
messages = [
|
||||||
|
{
|
||||||
|
"role": "user",
|
||||||
|
"content": "Write a Python function that checks whether a number is prime."
|
||||||
|
}
|
||||||
|
]
|
||||||
|
|
||||||
|
inputs = tokenizer.apply_chat_template(
|
||||||
|
messages,
|
||||||
|
tokenize=True,
|
||||||
|
add_generation_prompt=True,
|
||||||
|
return_tensors="pt"
|
||||||
|
).to(model.device)
|
||||||
|
|
||||||
|
with torch.no_grad():
|
||||||
|
output = model.generate(
|
||||||
|
input_ids=inputs,
|
||||||
|
max_new_tokens=1000,
|
||||||
|
temperature=0.7,
|
||||||
|
top_p=0.9,
|
||||||
|
repetition_penalty=1.1,
|
||||||
|
do_sample=True,
|
||||||
|
)
|
||||||
|
|
||||||
|
response = tokenizer.decode(
|
||||||
|
output[0][inputs.shape[1]:],
|
||||||
|
skip_special_tokens=True
|
||||||
|
)
|
||||||
|
print(response)
|
||||||
|
```
|
||||||
|
|
||||||
|
### Unsloth (faster inference)
|
||||||
|
|
||||||
|
```python
|
||||||
|
from unsloth import FastLanguageModel
|
||||||
|
import torch
|
||||||
|
|
||||||
|
model, tokenizer = FastLanguageModel.from_pretrained(
|
||||||
|
model_name="EphAsad/Atem-v1-1.5B",
|
||||||
|
max_seq_length=4096,
|
||||||
|
dtype=torch.bfloat16,
|
||||||
|
load_in_4bit=True,
|
||||||
|
)
|
||||||
|
FastLanguageModel.for_inference(model)
|
||||||
|
|
||||||
|
messages = [
|
||||||
|
{
|
||||||
|
"role": "user",
|
||||||
|
"content": "Explain the difference between a stack and a queue, with examples."
|
||||||
|
}
|
||||||
|
]
|
||||||
|
|
||||||
|
inputs = tokenizer.apply_chat_template(
|
||||||
|
messages,
|
||||||
|
tokenize=True,
|
||||||
|
add_generation_prompt=True,
|
||||||
|
return_tensors="pt"
|
||||||
|
).to("cuda")
|
||||||
|
|
||||||
|
with torch.no_grad():
|
||||||
|
output = model.generate(
|
||||||
|
input_ids=inputs,
|
||||||
|
max_new_tokens=1000,
|
||||||
|
temperature=0.7,
|
||||||
|
top_p=0.9,
|
||||||
|
do_sample=True,
|
||||||
|
)
|
||||||
|
|
||||||
|
print(tokenizer.decode(
|
||||||
|
output[0][inputs.shape[1]:],
|
||||||
|
skip_special_tokens=True
|
||||||
|
))
|
||||||
|
```
|
||||||
|
|
||||||
|
### Ollama
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Recommended — best speed/quality balance
|
||||||
|
ollama run hf.co/EphAsad/Atem-v1-1.5B:Q4_K_M
|
||||||
|
|
||||||
|
# Higher quality
|
||||||
|
ollama run hf.co/EphAsad/Atem-v1-1.5B:Q5_K_M
|
||||||
|
|
||||||
|
# Near-lossless
|
||||||
|
ollama run hf.co/EphAsad/Atem-v1-1.5B:Q8_0
|
||||||
|
```
|
||||||
|
|
||||||
|
### llama.cpp
|
||||||
|
|
||||||
|
```bash
|
||||||
|
llama-server -hf EphAsad/Atem-v1-1.5B:Q4_K_M
|
||||||
|
```
|
||||||
|
|
||||||
|
### System Prompt
|
||||||
|
|
||||||
|
Atem's identity is baked into the chat template and activates automatically when no system message is provided. For manual override:
|
||||||
|
|
||||||
|
```
|
||||||
|
You are Atem, a precise and analytical reasoning assistant. You approach
|
||||||
|
every problem methodically — identifying core concepts, reasoning step by
|
||||||
|
step, and arriving at well-supported conclusions. You show your thinking
|
||||||
|
clearly and are thorough, direct, and intellectually honest.
|
||||||
|
```
|
||||||
|
|
||||||
|
### Available Files
|
||||||
|
|
||||||
|
| File | Size | Description |
|
||||||
|
|------|------|-------------|
|
||||||
|
| `model.safetensors` | ~3.1 GB | Full bfloat16 merged weights |
|
||||||
|
| `Atem-1.5b.Q4_K_M.gguf` | ~986 MB | 4-bit quantised — recommended |
|
||||||
|
| `Atem-1.5b.Q5_K_M.gguf` | ~1.1 GB | 5-bit quantised |
|
||||||
|
| `Atem-1.5b.Q8_0.gguf` | ~1.6 GB | 8-bit quantised — near-lossless |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Known Limitations
|
||||||
|
|
||||||
|
**No thinking traces (Stage 1 by design).** Think tags were stripped from all training data for Stage 1. The model does not produce extended `<think>` reasoning traces. Stage 2 training will layer this capability on top of the Stage 1 foundation.
|
||||||
|
|
||||||
|
**Mathematical precision on complex problems.** On multi-step calculations, the model may make arithmetic slips in intermediate steps while arriving at a structurally correct approach. Answers to high-stakes mathematical problems should be independently verified.
|
||||||
|
|
||||||
|
**HellaSwag regression.** A 2.4% regression on HellaSwag commonsense completion is observed. This is minor and substantially better than the 16.2% regression produced by the earlier exploratory full fine-tune run, confirming that LoRA preserved base commonsense capability effectively.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Roadmap
|
||||||
|
|
||||||
|
Atem v1 establishes the Stage 1 foundation. Planned next steps:
|
||||||
|
|
||||||
|
- **Stage 2:** LoRA SFT on curated chain-of-thought data to add thinking trace capability — using `Complex_CoT`, `inverted_reasoning`, and reasoning trace columns held out from Stage 1 training
|
||||||
|
- **Extended benchmarks:** MMLU, BBH, IFEval, WinoGrande, MBPP post-Stage 2
|
||||||
|
- **Atem v2:** Expanded corpus, further domain coverage
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Citation
|
||||||
|
|
||||||
|
```bibtex
|
||||||
|
@misc{atem_v1_2026,
|
||||||
|
author = {Asad, Zain},
|
||||||
|
title = {Atem v1: A 1.5B Reasoning Model via
|
||||||
|
Multi-Source Knowledge Distillation},
|
||||||
|
year = {2026},
|
||||||
|
publisher = {HuggingFace},
|
||||||
|
howpublished = {\url{https://huggingface.co/EphAsad/Atem-v1-1.5B}},
|
||||||
|
}
|
||||||
|
```
|
||||||
|
---
|
||||||
|
|
||||||
|
## Support
|
||||||
|
|
||||||
|
If you find this model useful for your research or projects,
|
||||||
|
you can support further development of my datasets and models here:
|
||||||
|
☕ [ko-fi.com/ephraim123](https://ko-fi.com/ephraim123)
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## License
|
||||||
|
|
||||||
|
Released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0), consistent with the base model Qwen2.5-1.5B-Instruct.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
<p align="center">
|
||||||
|
Built independently by <a href="https://huggingface.co/EphAsad">EphAsad</a>
|
||||||
|
</p>
|
||||||
54
chat_template.jinja
Normal file
54
chat_template.jinja
Normal file
@@ -0,0 +1,54 @@
|
|||||||
|
{%- if tools %}
|
||||||
|
{{- '<|im_start|>system\n' }}
|
||||||
|
{%- if messages[0]['role'] == 'system' %}
|
||||||
|
{{- messages[0]['content'] }}
|
||||||
|
{%- else %}
|
||||||
|
{{- 'You are Atem, a precise and analytical reasoning assistant. You approach every problem methodically — identifying core concepts, reasoning step by step, and arriving at well-supported conclusions. You show your thinking clearly and are thorough, direct, and intellectually honest.' }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||||
|
{%- for tool in tools %}
|
||||||
|
{{- "\n" }}
|
||||||
|
{{- tool | tojson }}
|
||||||
|
{%- endfor %}
|
||||||
|
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||||
|
{%- else %}
|
||||||
|
{%- if messages[0]['role'] == 'system' %}
|
||||||
|
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
||||||
|
{%- else %}
|
||||||
|
{{- '<|im_start|>system\nYou are Atem, a precise and analytical reasoning assistant. You approach every problem methodically — identifying core concepts, reasoning step by step, and arriving at well-supported conclusions. You show your thinking clearly and are thorough, direct, and intellectually honest.<|im_end|>\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- for message in messages %}
|
||||||
|
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||||
|
{%- elif message.role == "assistant" %}
|
||||||
|
{{- '<|im_start|>' + message.role }}
|
||||||
|
{%- if message.content %}
|
||||||
|
{{- '\n' + message.content }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- for tool_call in message.tool_calls %}
|
||||||
|
{%- if tool_call.function is defined %}
|
||||||
|
{%- set tool_call = tool_call.function %}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '\n<tool_call>\n{"name": "' }}
|
||||||
|
{{- tool_call.name }}
|
||||||
|
{{- '", "arguments": ' }}
|
||||||
|
{{- tool_call.arguments | tojson }}
|
||||||
|
{{- '}\n</tool_call>' }}
|
||||||
|
{%- endfor %}
|
||||||
|
{{- '<|im_end|>\n' }}
|
||||||
|
{%- elif message.role == "tool" %}
|
||||||
|
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
||||||
|
{{- '<|im_start|>user' }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '\n<tool_response>\n' }}
|
||||||
|
{{- message.content }}
|
||||||
|
{{- '\n</tool_response>' }}
|
||||||
|
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||||
|
{{- '<|im_end|>\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- if add_generation_prompt %}
|
||||||
|
{{- '<|im_start|>assistant\n' }}
|
||||||
|
{%- endif %}
|
||||||
62
config.json
Normal file
62
config.json
Normal file
@@ -0,0 +1,62 @@
|
|||||||
|
{
|
||||||
|
"architectures": [
|
||||||
|
"Qwen2ForCausalLM"
|
||||||
|
],
|
||||||
|
"attention_dropout": 0.0,
|
||||||
|
"bos_token_id": null,
|
||||||
|
"torch_dtype": "bfloat16",
|
||||||
|
"eos_token_id": 151645,
|
||||||
|
"hidden_act": "silu",
|
||||||
|
"hidden_size": 1536,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 8960,
|
||||||
|
"layer_types": [
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention"
|
||||||
|
],
|
||||||
|
"max_position_embeddings": 32768,
|
||||||
|
"max_window_layers": 21,
|
||||||
|
"model_type": "qwen2",
|
||||||
|
"num_attention_heads": 12,
|
||||||
|
"num_hidden_layers": 28,
|
||||||
|
"num_key_value_heads": 2,
|
||||||
|
"pad_token_id": 151665,
|
||||||
|
"rms_norm_eps": 1e-06,
|
||||||
|
"rope_parameters": {
|
||||||
|
"rope_theta": 1000000.0,
|
||||||
|
"rope_type": "default"
|
||||||
|
},
|
||||||
|
"sliding_window": null,
|
||||||
|
"tie_word_embeddings": true,
|
||||||
|
"unsloth_fixed": true,
|
||||||
|
"unsloth_version": "2026.5.9",
|
||||||
|
"use_cache": false,
|
||||||
|
"use_sliding_window": false,
|
||||||
|
"vocab_size": 151936
|
||||||
|
}
|
||||||
14
generation_config.json
Normal file
14
generation_config.json
Normal file
@@ -0,0 +1,14 @@
|
|||||||
|
{
|
||||||
|
"do_sample": true,
|
||||||
|
"eos_token_id": [
|
||||||
|
151645,
|
||||||
|
151643
|
||||||
|
],
|
||||||
|
"max_length": 32768,
|
||||||
|
"pad_token_id": 151665,
|
||||||
|
"repetition_penalty": 1.1,
|
||||||
|
"temperature": 0.7,
|
||||||
|
"top_k": 20,
|
||||||
|
"top_p": 0.8,
|
||||||
|
"transformers_version": "5.5.0"
|
||||||
|
}
|
||||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:03933817bebd1ea2faa8434f923cc5aeccf4a8bd07a2793aeab0d98ad69f77dd
|
||||||
|
size 3087467144
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:bd5948af71b4f56cf697f7580814c7ce8b80595ef985544efcacf716126a2e31
|
||||||
|
size 11422356
|
||||||
202
tokenizer_config.json
Normal file
202
tokenizer_config.json
Normal file
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"is_local": false,
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"model_max_length": 32768,
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"pad_token": "<|PAD_TOKEN|>",
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"padding_side": "left",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null,
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"added_tokens_decoder": {
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"151643": {
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"content": "<|endoftext|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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},
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"151644": {
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"content": "<|im_start|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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},
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"151645": {
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"content": "<|im_end|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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},
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"151646": {
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"content": "<|object_ref_start|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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},
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"151647": {
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"content": "<|object_ref_end|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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},
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"151648": {
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"content": "<|box_start|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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},
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"151649": {
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"content": "<|box_end|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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},
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"151650": {
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"content": "<|quad_start|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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},
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"151651": {
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"content": "<|quad_end|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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},
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"151652": {
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"content": "<|vision_start|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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},
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"151653": {
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"content": "<|vision_end|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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},
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"151654": {
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"content": "<|vision_pad|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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},
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"151655": {
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"content": "<|image_pad|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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},
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"151656": {
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"content": "<|video_pad|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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},
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"151657": {
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"content": "<tool_call>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": false
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},
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"151658": {
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"content": "</tool_call>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": false
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},
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"151659": {
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"content": "<|fim_prefix|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": false
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},
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"151660": {
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"content": "<|fim_middle|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": false
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},
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"151661": {
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"content": "<|fim_suffix|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": false
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},
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"151662": {
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"content": "<|fim_pad|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": false
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},
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"151663": {
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"content": "<|repo_name|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": false
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},
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"151664": {
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"content": "<|file_sep|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": false
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},
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"151665": {
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"content": "<|PAD_TOKEN|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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}
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},
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"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Atem, a precise and analytical reasoning assistant. You approach every problem methodically — identifying core concepts, reasoning step by step, and arriving at well-supported conclusions. You show your thinking clearly and are thorough, direct, and intellectually honest.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Atem, a precise and analytical reasoning assistant. You approach every problem methodically — identifying core concepts, reasoning step by step, and arriving at well-supported conclusions. You show your thinking clearly and are thorough, direct, and intellectually honest.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
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}
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Reference in New Issue
Block a user