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FROM Atem-1.5b.Q4_K_M.gguf
TEMPLATE """{{- if .Messages }}
{{- if or .System .Tools }}<|im_start|>system
{{- if .System }}
{{ .System }}
{{- end }}
{{- if .Tools }}
# Tools
You may call one or more functions to assist with the user query.
You are provided with function signatures within <tools></tools> XML tags:
<tools>
{{- range .Tools }}
{"type": "function", "function": {{ .Function }}}
{{- end }}
</tools>
For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>
{{- end }}<|im_end|>
{{ end }}
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 -}}
{{- if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{ else if eq .Role "assistant" }}<|im_start|>assistant
{{ if .Content }}{{ .Content }}
{{- else if .ToolCalls }}<tool_call>
{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
{{ end }}</tool_call>
{{- end }}{{ if not $last }}<|im_end|>
{{ end }}
{{- else if eq .Role "tool" }}<|im_start|>user
<tool_response>
{{ .Content }}
</tool_response><|im_end|>
{{ end }}
{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
{{ end }}
{{- end }}
{{- else }}
{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
PARAMETER temperature 0.7
PARAMETER min_p 0.1
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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---
language:
- en
license: apache-2.0
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
tags:
- text-generation
- qwen2
- unsloth
- lora
- gguf
- llama.cpp
- reasoning
- distillation
- conversational
pipeline_tag: text-generation
library_name: transformers
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
model-index:
- name: Atem v1
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: ARC-Challenge
type: ai2_arc
config: ARC-Challenge
split: test
metrics:
- type: acc_norm
value: 0.455
name: Accuracy (normalised)
verified: false
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8K
type: gsm8k
split: test
metrics:
- type: exact_match
value: 0.530
name: Exact Match (strict, zero-shot)
verified: false
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag
type: hellaswag
split: validation
metrics:
- type: acc_norm
value: 0.644
name: Accuracy (normalised)
verified: false
---
<p align="center">
<img src="Logo.png" width="300" alt="Atem Logo"/>
</p>
<h1 align="center">Atem v1</h1>
<p align="center">
<em>Ancient logic. Modern intelligence.</em>
</p>
<p align="center">
A 1.5B reasoning model trained via multi-source knowledge distillation from frontier teacher models.
</p>
<p align="center">
<img src="https://img.shields.io/badge/Base-Qwen2.5--1.5B--Instruct-blue" alt="Base Model"/>
<img src="https://img.shields.io/badge/Method-LoRA%20SFT-purple" alt="Method"/>
<img src="https://img.shields.io/badge/Parameters-1.5B-orange" alt="Parameters"/>
<img src="https://img.shields.io/badge/License-Apache%202.0-green" alt="License"/>
</p>
---
## Overview
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.
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.
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.
---
## Model Details
| Property | Value |
|----------|-------|
| **Base model** | Qwen/Qwen2.5-1.5B-Instruct |
| **Training method** | LoRA Supervised Fine-Tuning (Stage 1) |
| **LoRA config** | r=32, alpha=64, dropout=0.05 |
| **Target modules** | q, k, v, o, gate, up, down projections |
| **Parameters** | ~1.54B |
| **Training records** | ~114,932 |
| **Epochs** | 1 |
| **Effective batch size** | 64 (batch 8 × grad accum 8) |
| **Learning rate** | 2e-4, cosine schedule, 5% warmup |
| **Final train loss** | 0.940 |
| **Final val loss** | 0.890 |
| **Hardware** | NVIDIA A100-SXM4 80GB |
| **Max sequence length** | 4,096 tokens |
| **Precision** | bfloat16 |
| **License** | Apache 2.0 |
---
## Intended Use
Atem is designed for open-ended reasoning tasks where structured, accurate thinking adds value:
- Code explanation, implementation, and debugging
- Mathematical problem solving with working shown
- Analytical reasoning and hypothesis evaluation
- Concept explanation and comparative analysis
- Logic, argument, and fallacy identification
Atem is **not** designed for retrieval-heavy factual lookup, real-time information, or tasks requiring broad knowledge breadth beyond its training domains.
---
## Training Data
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.
| Dataset | Records | Source / Teacher |
|---------|---------|-----------------|
| EphAsad/QWENMillenium-SF | 5,000 | Qwen2.5-14B — Analytical & Scientific |
| EphAsad/Phi4Millennium-SF | 2,932 | Phi-4 14B — Mathematical Reasoning |
| EphAsad/MistralMillenium-SF | 5,000 | Mistral-Nemo-12B — Language & Comprehension |
| Modotte/CodeX-2M-Thinking | 30,000 | Mixed — Coding |
| Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned | 23,000 | Kimi K2.5 — General Distillation (English filtered) |
| WithinUsAI/MiniMax_M2.7_Distilled_5k | 5,000 | MiniMax M2.7 |
| tuanha1305/DeepSeek-R1-Distill | 9,000 | DeepSeek-R1 |
| open-r1/OpenThoughts-114k-math | 10,000 | Mixed — Mathematics (correct answers only) |
| flytech/python-codes-25k | 10,000 | Python coding |
| FreedomIntelligence/medical-o1-reasoning-SFT | 10,000 | Medical reasoning (English config) |
| Private dataset | 5,000 | Undisclosed |
| **Total** | **~114,932** | |
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.
---
## Training Configuration
```python
# Key hyperparameters
lora_r = 32
lora_alpha = 64
lora_dropout = 0.05
max_seq_length = 4096
learning_rate = 2e-4
lr_scheduler = 'cosine'
warmup_ratio = 0.05
batch_size = 8
grad_accumulation = 8 # effective batch size: 64
num_epochs = 1
dtype = bfloat16
load_in_4bit = True # during training
```
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.
After training, LoRA adapters were merged into the base weights and exported as a full merged model.
**Loss curve:**
| Step | Train Loss | Val Loss |
|------|-----------|----------|
| 500 | 0.990 | 0.920 |
| 1000 | 1.020 | 0.900 |
| 1500 | 0.960 | 0.890 |
| Final | **0.940** | **0.890** |
Validation loss converged at 0.890, with a final train/val gap of 0.050 — indicating no overfitting over the single epoch.
---
## Evaluation
### Benchmark Results
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.
| Task | Base (1.5B) | Atem v1 (1.5B) | Delta |
|------|------------|----------------|-------|
| 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>

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{%- 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 %}

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{
"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": {
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"rope_type": "default"
},
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"tie_word_embeddings": true,
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"unsloth_version": "2026.5.9",
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 151936
}

14
generation_config.json Normal file
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{
"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
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tokenizer.json Normal file
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202
tokenizer_config.json Normal file
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{
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"is_local": false,
"model_max_length": 32768,
"pad_token": "<|PAD_TOKEN|>",
"padding_side": "left",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null,
"added_tokens_decoder": {
"151643": {
"content": "<|endoftext|>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": true
},
"151644": {
"content": "<|im_start|>",
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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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"content": "<|video_pad|>",
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},
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},
"151658": {
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},
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},
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"content": "<|fim_middle|>",
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},
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"content": "<|file_sep|>",
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},
"151665": {
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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"
}