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Model: EphAsad/Atem-3B Source: Original Platform
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FROM Atem-3b.Q8_0.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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|
||||
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 1.5
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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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363
README.md
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README.md
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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: Qwen/Qwen2.5-3B-Instruct
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tags:
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- text-generation
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- transformers
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||||
- safetensors
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||||
- gguf
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- qwen2
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- unsloth
|
||||
- lora
|
||||
- llama.cpp
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||||
- reasoning
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||||
- distillation
|
||||
- conversational
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||||
datasets:
|
||||
- EphAsad/QWENMillenium-SF
|
||||
- EphAsad/Phi4Millennium-SF
|
||||
- EphAsad/MistralMillenium-SF
|
||||
- Modotte/CodeX-2M-Thinking
|
||||
- Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned
|
||||
- WithinUsAI/MiniMax_M2.7_Distilled_5k
|
||||
- tuanha1305/DeepSeek-R1-Distill
|
||||
- open-r1/OpenThoughts-114k-math
|
||||
- flytech/python-codes-25k
|
||||
- FreedomIntelligence/medical-o1-reasoning-SFT
|
||||
- Jackrong/Claude-opus-4.7-TraceInversion-5000x
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pipeline_tag: text-generation
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model-index:
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- name: EphAsad/Atem-3B
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results:
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||||
- task:
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type: text-generation
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||||
dataset:
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||||
name: ARC-Challenge
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||||
type: allenai/ai2_arc
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||||
config: ARC-Challenge
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||||
split: test
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||||
metrics:
|
||||
- type: acc_norm
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||||
name: Accuracy (normalised)
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||||
value: 0.480
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||||
verified: false
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||||
- task:
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||||
type: text-generation
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||||
dataset:
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||||
name: GSM8K
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||||
type: openai/gsm8k
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||||
config: main
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||||
split: test
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||||
metrics:
|
||||
- type: exact_match
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||||
name: Exact Match (flexible-extract, 5-shot)
|
||||
value: 0.647
|
||||
verified: false
|
||||
- task:
|
||||
type: text-generation
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||||
dataset:
|
||||
name: HellaSwag
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||||
type: Rowan/hellaswag
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||||
split: validation
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||||
metrics:
|
||||
- type: acc_norm
|
||||
name: Accuracy (normalised)
|
||||
value: 0.704
|
||||
verified: false
|
||||
---
|
||||
|
||||
|
||||
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||||
# Atem-3B
|
||||
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||||
*Ancient logic. Modern intelligence.*
|
||||
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||||
The 3B foundation model of the Atem series — direct reasoning at scale.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
Atem-3B is the first release in the 3B branch of the Atem model series — a Stage 1 supervised fine-tune on Qwen2.5-3B-Instruct across approximately 120,000 training examples spanning mathematics, code, reasoning, and general instruction following.
|
||||
|
||||
Where the 1.5B Atem line demonstrated that a small model could be meaningfully improved through careful data curation, Atem-3B applies the same methodology at twice the parameter count. The 3B base provides a stronger foundation — particularly for mathematical reasoning and structured generation — while the training corpus prioritises quality and diversity over volume.
|
||||
|
||||
**Design philosophy:** Think tags were stripped from all training data during preprocessing. Atem-3B is a direct-answer model — it does not produce `<think>` traces. The reasoning capacity of the 3B base is channelled into producing well-structured, considered responses rather than visible chain-of-thought. A CoT variant is planned for Stage 2.
|
||||
|
||||
---
|
||||
|
||||
## The Atem Series
|
||||
|
||||
**1.5B Series**
|
||||
|
||||
| Model | Stage | Capability |
|
||||
|---|---|---|
|
||||
| [Atem v1](https://huggingface.co/EphAsad/Atem-v1-1.5B) | Stage 1 — SFT | Fast, direct reasoning |
|
||||
| [Atem-Wisdom](https://huggingface.co/EphAsad/Atem-Wisdom-1.5B) | Stage 2 — CoT | Explicit thinking traces |
|
||||
| Atem-Pharaoh *(planned)* | Stage 3 — DPO/IPO | Preference-aligned reasoning |
|
||||
|
||||
**3B Series**
|
||||
|
||||
| Model | Stage | Capability |
|
||||
|---|---|---|
|
||||
| **Atem-3B** | Stage 1 — SFT | Direct reasoning at 3B scale |
|
||||
| **Atem-3B-Pharaoh** | Stage 2 — CoT | Explicit thinking traces |
|
||||
|
||||
---
|
||||
|
||||
## Model Details
|
||||
|
||||
| Property | Value |
|
||||
|---|---|
|
||||
| **Base model** | Qwen/Qwen2.5-3B-Instruct |
|
||||
| **Training method** | LoRA SFT — Stage 1 (think tags stripped) |
|
||||
| **LoRA config** | r=32, alpha=64, dropout=0.05 |
|
||||
| **Parameters** | ~3.09B |
|
||||
| **Trainable parameters** | 59,867,136 (1.90%) |
|
||||
| **Training records** | 120,043 (after token length filtering) |
|
||||
| **Epochs** | 1 |
|
||||
| **Final val loss** | 0.8384 |
|
||||
| **Hardware** | NVIDIA A100-SXM4-80GB |
|
||||
| **Max sequence length** | 4,096 tokens |
|
||||
| **Precision** | bfloat16 |
|
||||
| **License** | Apache 2.0 |
|
||||
|
||||
---
|
||||
|
||||
## Output Format
|
||||
|
||||
Atem-3B produces direct, structured responses. Think tags were stripped from all training data during preprocessing — the model was trained exclusively on clean outputs with no chain-of-thought traces.
|
||||
|
||||
```
|
||||
[Direct response — reasoned, structured, no <think> tags]
|
||||
```
|
||||
|
||||
This is a deliberate Stage 1 design choice. A chain-of-thought variant exposing explicit reasoning traces is planned as Stage 2.
|
||||
|
||||
---
|
||||
|
||||
## Training Data
|
||||
|
||||
Stage 1 training used approximately 120,000 examples drawn from eleven sources. All reasoning traces (`<think>...</think>` blocks) were stripped prior to training. Records shorter than 20 characters after stripping were excluded.
|
||||
|
||||
| Dataset | Count | Focus |
|
||||
|---|---|---|
|
||||
| Modotte/CodeX-2M-Thinking | 40,000 | Code (think tags stripped) |
|
||||
| Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned | 23,000 | General reasoning (English filtered) |
|
||||
| open-r1/OpenThoughts-114k-math | 10,000 | Mathematics (correct only) |
|
||||
| flytech/python-codes-25k | 10,000 | Python code |
|
||||
| FreedomIntelligence/medical-o1-reasoning-SFT | 10,000 | Medical reasoning |
|
||||
| tuanha1305/DeepSeek-R1-Distill | 9,000 | Reasoning distillation |
|
||||
| EphAsad/QWENMillenium-SF | 5,000 | General instruction |
|
||||
| EphAsad/MistralMillenium-SF | 5,000 | General instruction |
|
||||
| WithinUsAI/MiniMax_M2.7_Distilled_5k | 5,000 | Mixed reasoning |
|
||||
| Jackrong/Claude-opus-4.7-TraceInversion-5000x | 4,761 | Inverted reasoning |
|
||||
| EphAsad/Phi4Millennium-SF | 2,932 | General instruction |
|
||||
|
||||
Chinese-language records from Kimi K2.5 were filtered using an ASCII character ratio threshold before inclusion. OpenThoughts-114k-math was filtered to `correct == True` examples only.
|
||||
|
||||
**Loss curve:**
|
||||
|
||||
| Step | Train Loss | Val Loss |
|
||||
|---|---|---|
|
||||
| 200 | 0.9236 | 0.9011 |
|
||||
| 400 | 0.9200 | 0.8796 |
|
||||
| 600 | 0.8591 | 0.8685 |
|
||||
| 800 | 0.8837 | 0.8585 |
|
||||
| 1000 | 0.8455 | 0.8507 |
|
||||
| 1200 | 0.8359 | 0.8453 |
|
||||
| 1400 | 0.8240 | 0.8413 |
|
||||
| 1600 | 0.8626 | 0.8391 |
|
||||
| 1800 | 0.8940 | 0.8384 |
|
||||
| 1876 (final) | **0.8702** | **0.8384** |
|
||||
|
||||
Validation loss descends steadily throughout the full run with no overfitting signal.
|
||||
|
||||
---
|
||||
|
||||
## Evaluation
|
||||
|
||||
### Benchmark Results
|
||||
|
||||
Evaluated using lm-evaluation-harness via the Python API under identical conditions for both models. ARC-Challenge and HellaSwag use zero-shot normalised accuracy; GSM8K uses 5-shot. Both models evaluated at 4-bit quantisation on the same A100-SXM4-80GB in torch.float16.
|
||||
|
||||
| Task | Base (3B) | Atem-3B | Delta |
|
||||
|---|---|---|---|
|
||||
| ARC-Challenge | 48.1% | 48.0% | -0.1% — |
|
||||
| GSM8K (strict-match) | 2.1% | 37.1% | +35.0% |
|
||||
| GSM8K (flexible-extract) | 62.4% | **64.7%** | +2.3% ✓ |
|
||||
| HellaSwag | 73.5% | 70.4% | -3.0% ⚠ |
|
||||
|
||||
**Note on GSM8K:** lm_eval's strict-match filter uses a `#### number` regex that only fires when the model produces that exact token sequence. The base Qwen2.5-3B-Instruct solves problems correctly but formats answers conversationally, yielding 2.1% strict-match against a 62.4% flexible-extract — the latter being the accurate measure of base model mathematical capability. Atem-3B's training on math distillation datasets reinforced structured answer termination, producing 37.1% strict-match. The meaningful comparison is flexible-extract: **62.4% → 64.7% (+2.3%)** — a genuine but modest improvement. The strict-match delta is a formatting artefact, not a 35-point gain in mathematical reasoning ability.
|
||||
|
||||
**Note on HellaSwag:** The -3.0% regression is a common pattern when fine-tuning instruct models on structured reasoning and task-completion data. HellaSwag tests commonsense sentence completion in a multiple-choice format; training on problem-solving corpora shifts the model's distribution away from the casual, predictive register that HellaSwag measures. This is a known trade-off, not an indicator of general capability loss.
|
||||
|
||||
---
|
||||
|
||||
## Usage
|
||||
|
||||
### Transformers
|
||||
|
||||
```python
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
import torch
|
||||
|
||||
model_name = "EphAsad/Atem-3B"
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_name,
|
||||
torch_dtype=torch.bfloat16,
|
||||
device_map="auto"
|
||||
)
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Explain the difference between a process and a thread."
|
||||
}
|
||||
]
|
||||
|
||||
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=1024,
|
||||
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-3B",
|
||||
max_seq_length=4096,
|
||||
dtype=torch.bfloat16,
|
||||
load_in_4bit=True,
|
||||
)
|
||||
FastLanguageModel.for_inference(model)
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Write a Python function to find all prime numbers up to n."
|
||||
}
|
||||
]
|
||||
|
||||
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=1024,
|
||||
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-3B:Q4_K_M
|
||||
|
||||
# Higher quality
|
||||
ollama run hf.co/EphAsad/Atem-3B:Q5_K_M
|
||||
|
||||
# Near-lossless
|
||||
ollama run hf.co/EphAsad/Atem-3B:Q8_0
|
||||
```
|
||||
|
||||
### llama.cpp
|
||||
|
||||
```bash
|
||||
llama-server -hf EphAsad/Atem-3B:Q4_K_M
|
||||
```
|
||||
|
||||
### Available Files
|
||||
|
||||
| File | Size | Description |
|
||||
|---|---|---|
|
||||
| `model-00001-of-00002.safetensors` + `model-00002-of-00002.safetensors` | ~6.2 GB | Full bfloat16 weights |
|
||||
| `Atem-3b.Q4_K_M.gguf` | ~1.93 GB | 4-bit — recommended |
|
||||
| `Atem-3b.Q5_K_M.gguf` | ~2.22 GB | 5-bit |
|
||||
| `Atem-3b.Q8_0.gguf` | ~3.29 GB | 8-bit — near-lossless |
|
||||
|
||||
### System Prompt
|
||||
|
||||
Atem-3B's identity is baked into the chat template and activates without an explicit system message. To override manually:
|
||||
|
||||
```
|
||||
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.
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Roadmap
|
||||
|
||||
| Stage | Status | Description |
|
||||
|---|---|---|
|
||||
| Stage 1 — SFT | ✅ Complete | **Atem-3B — this model** |
|
||||
| Stage 2 — CoT SFT | 🔄 Planned | Atem-3B-Wisdom — chain-of-thought traces |
|
||||
| Stage 3 — DPO/IPO | 🔄 Planned | Atem-3B-Pharaoh — preference-aligned reasoning |
|
||||
|
||||
---
|
||||
|
||||
## Citation
|
||||
|
||||
```bibtex
|
||||
@misc{atem_3b_2026,
|
||||
author = {Asad, Zain},
|
||||
title = {Atem-3B: A 3B Direct-Reasoning Model via Stage 1 SFT},
|
||||
year = {2026},
|
||||
publisher = {HuggingFace},
|
||||
howpublished = {\url{https://huggingface.co/EphAsad/Atem-3B}},
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## License
|
||||
|
||||
Released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0), consistent with the base model (Qwen2.5-3B-Instruct).
|
||||
|
||||
---
|
||||
|
||||
Built independently by [EphAsad](https://huggingface.co/EphAsad)
|
||||
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 %}
|
||||
70
config.json
Normal file
70
config.json
Normal file
@@ -0,0 +1,70 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2ForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": null,
|
||||
"torch_dtype": "bfloat16",
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2048,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 11008,
|
||||
"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",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 70,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 36,
|
||||
"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": "2026.5.5",
|
||||
"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": 1.05,
|
||||
"temperature": 0.7,
|
||||
"top_k": 20,
|
||||
"top_p": 0.8,
|
||||
"transformers_version": "5.5.0"
|
||||
}
|
||||
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c9775e21a06f33fdbb064ea636a24f6d51960980dc29ef3de61e0a84bab9f72a
|
||||
size 3968658944
|
||||
3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:73c56f03a08b201a4c48c8dc18b2fe237886178d2d34991ca22c3f8e028d8687
|
||||
size 2203268048
|
||||
441
model.safetensors.index.json
Normal file
441
model.safetensors.index.json
Normal file
@@ -0,0 +1,441 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_size": 6171877376
|
||||
},
|
||||
"weight_map": {
|
||||
"model.embed_tokens.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
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|
||||
"model.layers.1.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
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|
||||
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|
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|
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|
||||
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|
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<|PAD_TOKEN|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"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"
|
||||
}
|
||||
Reference in New Issue
Block a user