commit 6019e000638a90897caa01e5ac0a7617090261e1 Author: ModelHub XC Date: Tue Jul 7 17:55:17 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: EphAsad/Atem-Wisdom-1.5B Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..5dab7ad --- /dev/null +++ b/.gitattributes @@ -0,0 +1,43 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text +Atem-v1-1.5B.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text +Atem-v1-1.5B.Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text +Atem-v1-1.5B.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text +Atem-Wisdom-1.5B.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text +Atem-Wisdom-1.5B.Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text +Atem-Wisdom-1.5B.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text +Logo.png filter=lfs diff=lfs merge=lfs -text diff --git a/Atem-Wisdom-1.5B.Q4_K_M.gguf b/Atem-Wisdom-1.5B.Q4_K_M.gguf new file mode 100644 index 0000000..9760121 --- /dev/null +++ b/Atem-Wisdom-1.5B.Q4_K_M.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ae56ab5444f200ecbf435809965b3b9358c9a00c83eeeaf0cc4bd99a7bb29d43 +size 986048544 diff --git a/Atem-Wisdom-1.5B.Q5_K_M.gguf b/Atem-Wisdom-1.5B.Q5_K_M.gguf new file mode 100644 index 0000000..ccc0253 --- /dev/null +++ b/Atem-Wisdom-1.5B.Q5_K_M.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:79c3eaab5bcebbd5d4cc3fe49996315d1534d6b9942e580bd5fe5849d0f214fa +size 1125050400 diff --git a/Atem-Wisdom-1.5B.Q8_0.gguf b/Atem-Wisdom-1.5B.Q8_0.gguf new file mode 100644 index 0000000..2fa3902 --- /dev/null +++ b/Atem-Wisdom-1.5B.Q8_0.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bcdd7efb0a907b822383d659c9d5a3b5c19732470922cbf4fac7230af6b718a2 +size 1646573088 diff --git a/Logo.png b/Logo.png new file mode 100644 index 0000000..2f31b1d --- /dev/null +++ b/Logo.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:036d268ac79d1a5355a5ea602fc02ba312f4938c3506ddfa148ec7de44b69607 +size 981796 diff --git a/README.md b/README.md new file mode 100644 index 0000000..4347198 --- /dev/null +++ b/README.md @@ -0,0 +1,399 @@ +--- +language: +- en +license: apache-2.0 +base_model: +- EphAsad/Atem-v1-1.5B +tags: +- text-generation +- qwen2 +- unsloth +- lora +- gguf +- llama.cpp +- reasoning +- chain-of-thought +- thinking +- distillation +pipeline_tag: text-generation +library_name: transformers +datasets: +- open-r1/OpenThoughts-114k-math +- Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned +- Modotte/CodeX-2M-Thinking +- FreedomIntelligence/medical-o1-reasoning-SFT +- WithinUsAI/MiniMax_M2.7_Distilled_5k +- nvidia/OpenCodeReasoning +model-index: +- name: Atem-Wisdom v1.5B + 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: 0.447 + name: Accuracy (normalised) + verified: false + - task: + type: text-generation + name: Text Generation + dataset: + name: GSM8K + type: gsm8k + split: test + metrics: + - type: exact_match + value: 0.519 + name: Exact Match (strict, 5-shot) + verified: false + - task: + type: text-generation + name: Text Generation + dataset: + name: HellaSwag + type: hellaswag + split: validation + metrics: + - type: acc_norm + value: 0.651 + name: Accuracy (normalised) + verified: false +--- + +

+ Atem Logo +

+ +

Atem-Wisdom

+ +

+ Ancient logic. Modern intelligence. +

+ +

+ The reasoning variant of Atem — a 1.5B model that thinks before it answers. +

+ +

+ Base Model + Stage + Parameters + License +

+ +--- + +## Overview + +Atem-Wisdom is the second release in the Atem model series — the reasoning variant of [Atem v1](https://huggingface.co/EphAsad/Atem-v1-1.5B). Where Atem v1 provides fast, direct answers, Atem-Wisdom reasons through problems step by step before responding, making its thinking process visible and auditable. + +The defining feature is the `` tag: before producing a final answer, the model works through the problem internally, considering approaches, catching intermediate errors, and arriving at a considered conclusion. This reasoning trace is shown in full, not hidden. + +**When to choose Atem-Wisdom over Atem v1:** +- Problems that benefit from explicit reasoning steps — mathematics, logic, analytical questions +- Situations where seeing the working matters as much as the answer +- Complex multi-part problems where intermediate reasoning affects the conclusion +- Tasks where you want to audit the model's reasoning, not just its output + +**When to choose Atem v1:** +- Routine tasks where speed matters more than depth +- Simple factual questions and direct coding tasks +- Constrained environments where output length is a concern + +--- + +## The Atem Series + +| Model | Stage | Capability | +|-------|-------|-----------| +| [Atem v1](https://huggingface.co/EphAsad/Atem-v1-1.5B) | Stage 1 — SFT | Fast, direct reasoning | +| **Atem-Wisdom** | Stage 2 — CoT | Explicit thinking traces | +| Atem-Pharaoh *(planned)* | Stage 3 — DPO/IPO | Preference-aligned reasoning | + +--- + +## Model Details + +| Property | Value | +|----------|-------| +| **Base model** | EphAsad/Atem-v1-1.5B | +| **Training method** | LoRA SFT — Stage 2 (Chain-of-Thought) | +| **LoRA config** | r=32, alpha=64, dropout=0.05 | +| **Parameters** | ~1.54B | +| **Training records** | ~38,000 (after token length filtering) | +| **Think / no-think split** | 75% / 25% | +| **Epochs** | 2 | +| **Final val loss** | 1.057 | +| **Hardware** | NVIDIA A100-SXM4 80GB | +| **Max sequence length** | 4,096 tokens | +| **Precision** | bfloat16 | +| **License** | Apache 2.0 | + +--- + +## Output Format + +Atem-Wisdom produces responses in one of two formats depending on problem complexity: + +**With reasoning trace (majority of responses):** +``` + +[Extended reasoning — working through the problem, identifying +approaches, checking intermediate steps, considering edge cases] + + +[Final answer — clear, direct, informed by the reasoning above] +``` + +**Direct answer (simple questions):** +``` +[Concise direct response — no reasoning trace needed] +``` + +The model calibrated this behaviour during training, with 75% of training examples including explicit think traces and 25% formatted as direct answers. In qualitative evaluation, 25 of 30 test questions produced think traces, with the 5 direct answers all being appropriately simple questions. + +--- + +## Training Data + +Stage 2 training used a corpus of approximately 38,000 chain-of-thought examples drawn from eight sources, assembled on top of Atem v1's Stage 1 foundation. All records were formatted to the `...` structure where applicable, with records exceeding 4,096 tokens removed rather than truncated. + +| Dataset | Focus | +|---------|-------| +| open-r1/OpenThoughts-114k-math | Mathematical reasoning | +| Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned | General reasoning (3 configs) | +| Modotte/CodeX-2M-Thinking | Coding with thinking traces | +| FreedomIntelligence/medical-o1-reasoning-SFT | Medical reasoning | +| WithinUsAI/MiniMax_M2.7_Distilled_5k | Mixed reasoning | +| nvidia/OpenCodeReasoning | Code reasoning | +| Private dataset | Inverted reasoning traces | + +Chinese-language reasoning traces from Kimi K2.5 were filtered using an ASCII character ratio threshold before inclusion. + +**Loss curve:** + +| Step | Train Loss | Val Loss | +|------|-----------|----------| +| 250 | 1.110 | 1.107 | +| 500 | 1.120 | 1.077 | +| 750 | 1.041 | 1.064 | +| 1000 | 1.045 | 1.058 | +| 1190 (final) | **1.039** | **1.057** | + +Two epochs were run after the single-epoch run showed val loss still declining at completion, indicating further improvement available. The final val loss of 1.057 represents meaningful improvement over the single-epoch result of 1.085. + +--- + +## Evaluation + +### Benchmark Results + +Evaluated using lm-evaluation-harness under identical conditions to Atem v1. ARC-Challenge and HellaSwag use zero-shot; GSM8K uses 5-shot. + +| Task | Base (1.5B) | Atem v1 | **Atem-Wisdom** | v1→Wisdom | +|------|------------|---------|-----------------|-----------| +| ARC-Challenge | 43.7% | 45.5% | **44.7%** | -0.8% | +| GSM8K (strict) | 23.0% | 53.0% | **51.9%** | -1.1% | +| GSM8K (flexible) | — | — | **53.6%** | +0.6% | +| HellaSwag | 66.8% | 64.4% | **65.1%** | +0.7% | + +**Note on GSM8K:** The strict match parser expects answers in `#### number` format. Atem-Wisdom's think traces cause answers to appear in a different structural position, which the strict parser occasionally misidentifies. The flexible extract score of 53.6% — which accepts any final numeric value — better reflects actual mathematical reasoning capability and slightly exceeds Atem v1's 53.0% strict score. HellaSwag shows marginal improvement from v1. ARC regression of 0.8% is within normal benchmark variance. + +### Qualitative Evaluation + +Atem-Wisdom was evaluated across 30 domain-representative questions using a matched system prompt (identical to the base model comparison), ensuring output differences reflect trained capability rather than prompt engineering. + +| Metric | Atem v1 | Atem-Wisdom | +|--------|---------|-------------| +| Avg response length | 349 words | 654 words | +| Think tags present | 0/30 | 25/30 | +| Min response | 10 words | 117 words | + +**Qualitative improvements over Atem v1:** + +- **Monty Hall problem:** Atem v1 incorrectly set up the problem with 2 doors. Atem-Wisdom correctly reasons through the 3-door setup and arrives at the correct 2/3 switching probability. +- **Differentiation:** Correctly derives f'(x) = x²(3ln(x)+1) and stationary point at x = e^(-1/3) with second-derivative confirmation, consistent across all versions from v1.1 onward. +- **Sky colour:** Atem-Wisdom correctly explains Rayleigh scattering for both daytime blue and sunset red/orange, where previous versions produced partially incorrect explanations. +- **Logical fallacy identification:** Correctly identifies argumentum ad populum (appeal to popularity) in a test argument. Prior versions were inconsistent on this question. +- **Calibrated reasoning traces:** The model correctly suppresses think traces on simple questions (geometric series, basic decorator implementation, colour physics) while applying extended reasoning to complex ones. + +**Known limitations:** + +- Specific arithmetic errors persist on a subset of mathematical problems (harmonic mean of speeds, circular permutations). These are targeted for Stage 3 preference training. +- Inference is significantly slower than Atem v1 due to longer outputs including reasoning traces. This is a fundamental property of reasoning models, not a fixable defect. + +--- + +## Usage + +### Transformers + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer +import torch + +model_name = "EphAsad/Atem-Wisdom-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": "A train travels from A to B at 60 km/h and returns " + "at 90 km/h. What is the average speed for the whole journey?" + } +] + +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=1500, + 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-Wisdom-1.5B", + max_seq_length=4096, + dtype=torch.bfloat16, + load_in_4bit=True, +) +FastLanguageModel.for_inference(model) + +messages = [ + { + "role": "user", + "content": "Explain the intuition behind the Monty Hall problem." + } +] + +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=1500, + 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-Wisdom-1.5B:Q4_K_M + +# Higher quality +ollama run hf.co/EphAsad/Atem-Wisdom-1.5B:Q5_K_M + +# Near-lossless +ollama run hf.co/EphAsad/Atem-Wisdom-1.5B:Q8_0 +``` + +### llama.cpp + +```bash +llama-server -hf EphAsad/Atem-Wisdom-1.5B:Q4_K_M +``` + +### Available Files + +| File | Size | Description | +|------|------|-------------| +| `model.safetensors` | ~3.1 GB | Full bfloat16 weights | +| `Atem-Wisdom-1.5B.Q4_K_M.gguf` | ~986 MB | 4-bit — recommended | +| `Atem-Wisdom-1.5B.Q5_K_M.gguf` | ~1.1 GB | 5-bit | +| `Atem-Wisdom-1.5B.Q8_0.gguf` | ~1.6 GB | 8-bit — near-lossless | + +### System Prompt + +Atem-Wisdom's identity and reasoning style are baked into the chat template and activate automatically without a 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 v1 — direct reasoning foundation | +| Stage 1.1 — Targeted SFT | ✅ Complete | Atem v1.1 — correctness improvements | +| Stage 2 — CoT SFT | ✅ Complete | **Atem-Wisdom — this model** | +| Stage 3 — DPO/IPO | 🔄 Planned | Atem-Pharaoh — preference-aligned reasoning | + +Stage 3 will apply Direct Preference Optimization and Identity Preference Optimization to further refine reasoning quality, specifically targeting the remaining mathematical precision errors identified in Stage 2 evaluation. + +--- + +## Citation + +```bibtex +@misc{atem_wisdom_2026, + author = {Asad, Zain}, + title = {Atem-Wisdom: A 1.5B Reasoning Model with + Explicit Chain-of-Thought Traces}, + year = {2026}, + publisher = {HuggingFace}, + howpublished = {\url{https://huggingface.co/EphAsad/Atem-Wisdom-1.5B}}, +} +``` + +--- + +## License + +Released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0), consistent with the base model chain (Qwen2.5-1.5B-Instruct → Atem v1 → Atem-Wisdom). + +--- + +

+ Built independently by EphAsad +

\ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..c37cec5 --- /dev/null +++ b/chat_template.jinja @@ -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 XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|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\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {{- tool_call.arguments | tojson }} + {{- '}\n' }} + {%- endfor %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- message.content }} + {{- '\n' }} + {%- 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 %} diff --git a/config.json b/config.json new file mode 100644 index 0000000..0fc39ab --- /dev/null +++ b/config.json @@ -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", + 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"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|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "151645": { + "content": "<|im_end|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "151646": { + "content": "<|object_ref_start|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "151647": { + "content": "<|object_ref_end|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "151648": { + "content": "<|box_start|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "151649": { + "content": "<|box_end|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "151650": { + "content": "<|quad_start|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "151651": { + "content": "<|quad_end|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "151652": { + "content": "<|vision_start|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "151653": { + "content": "<|vision_end|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "151654": { + "content": "<|vision_pad|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "151655": { + "content": "<|image_pad|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "151656": { + "content": "<|video_pad|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + "151657": { + "content": "", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": false + }, + "151658": { + "content": "", + "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 XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|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\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n' }}\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\\n' }}\n {{- message.content }}\n {{- '\\n' }}\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" +} \ No newline at end of file