commit cd552616a9b6c2ccddde95c6484807c9870c7b27 Author: ModelHub XC Date: Fri Jul 10 01:28:10 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: EphAsad/Midas-FableAgent-8B Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..9cb5047 --- /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-8B.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text +Atem-8B.Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text +Atem-8B.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text +Midas-FableAgent.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text +Midas-FableAgent.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text +Midas-FableAgent.Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text +Logo.png filter=lfs diff=lfs merge=lfs -text diff --git a/Logo.png b/Logo.png new file mode 100644 index 0000000..26a5c03 --- /dev/null +++ b/Logo.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:598c574e412fb89f0dfa9a56224f11a396c0aaf00470c675406fb3a98aa7e253 +size 1197391 diff --git a/Midas-FableAgent.Q4_K_M.gguf b/Midas-FableAgent.Q4_K_M.gguf new file mode 100644 index 0000000..3cdda20 --- /dev/null +++ b/Midas-FableAgent.Q4_K_M.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8573e21027f6762d773789d3ae88a19a7edd62280a2c80b8081427bef246ee41 +size 5027784704 diff --git a/Midas-FableAgent.Q5_K_M.gguf b/Midas-FableAgent.Q5_K_M.gguf new file mode 100644 index 0000000..d93d3e9 --- /dev/null +++ b/Midas-FableAgent.Q5_K_M.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:102f279b26b3543b9cc4ccf28789bff31657798b9d3316de7105a0faea15499b +size 5851113472 diff --git a/Midas-FableAgent.Q8_0.gguf b/Midas-FableAgent.Q8_0.gguf new file mode 100644 index 0000000..07a4b0e --- /dev/null +++ b/Midas-FableAgent.Q8_0.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:77d780723879e0f8e99a3ace0ba3d38367ac3d5b3dcafaef04ab56a629dac6f5 +size 8709519360 diff --git a/README.md b/README.md new file mode 100644 index 0000000..ebc79dc --- /dev/null +++ b/README.md @@ -0,0 +1,358 @@ +--- +language: +- en +license: apache-2.0 +base_model: +- EphAsad/Atem-8B +tags: +- qwen3 +- unsloth +- lora +- reasoning +- agentic +- conversational +- text-generation +- tool-use +pipeline_tag: text-generation +datasets: +- open-thoughts/OpenThoughts-Agent-v1-SFT +- kelexine/fable-5-sft-traces +- Glint-Research/Fable-5-traces +- armand0e/claude-fable-5-claude-code +--- + +# Midas-FableAgent + +*Plan. Act. Observe. Complete.* + +An agentic specialisation of [Atem-8B](https://huggingface.co/EphAsad/Atem-8B) — sequential fine-tuned for multi-step task execution, structured action emission, and observation-grounded iteration. Uses Fable agent traces. + +[![Base Model](https://img.shields.io/badge/Base-Atem--8B-blue)](https://huggingface.co/EphAsad/Atem-8B) +[![Method](https://img.shields.io/badge/Method-Sequential%20Agentic%20SFT-purple)](https://img.shields.io/badge/Method-Sequential%20Agentic%20SFT-purple) +[![Parameters](https://img.shields.io/badge/Parameters-8B-orange)](https://img.shields.io/badge/Parameters-8B-orange) +[![License](https://img.shields.io/badge/License-Apache%202.0-green)](https://www.apache.org/licenses/LICENSE-2.0) + +[![Midas-FableAgent Logo](https://huggingface.co/EphAsad/Midas-FableAgent/resolve/main/Logo.png)](https://huggingface.co/EphAsad/Midas-FableAgent/resolve/main/Logo.png) + +--- + +## Overview + +Midas-FableAgent is a sequential fine-tune of [Atem-8B](https://huggingface.co/EphAsad/Atem-8B) toward agentic task execution. Where Atem-8B is a general-purpose reasoning model, Midas-FableAgent is trained to operate in execution loops: receiving a task, reasoning about the current state, emitting structured actions, observing results, and iterating until completion. + +Training used two complementary data streams: + +- **Stream A** — 10,000 multi-turn agentic execution trajectories from [OpenThoughts-Agent-v1-SFT](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-SFT). Each trajectory is a full ReAct-style loop: task → JSON action → environment observation → JSON action → ... → `task_complete: true`. The model trains on every assistant turn in every trajectory, grounding its actions in real terminal output. + +- **Stream B** — 4,665 planning and CoT reasoning examples from [kelexine/fable-5-sft-traces](https://huggingface.co/datasets/kelexine/fable-5-sft-traces). Single or multi-turn examples with full `` traces, covering high-level task decomposition before any execution loop begins. + +Together these streams teach the model to *plan before acting* and *execute through observation* — the two capabilities that define reliable agentic behaviour. + +**Design note:** This is v2 of Midas-FableAgent. The primary known limitation is that approximately 69% of training examples were removed post-formatting because response tokens fell outside the context window after truncation — effectively training on ~4,100 examples rather than the full 14,665. A v3 using trajectory splitting (each assistant turn as an independent training example) is planned and will substantially increase effective training data. The current model demonstrates correct agentic format and reasoning patterns; it is undertrained relative to what the data should deliver. + +--- + +## Atem Ecosystem + +Midas-FableAgent is a task-specialised derivative of the Atem series, not a numbered Atem release. + +| Model | Type | Capability | +|---|---|---| +| [Atem-0.6B](https://huggingface.co/EphAsad/Atem-0.6B) | Qwen3 SFT | Compact reasoning | +| [Atem-1.7B](https://huggingface.co/EphAsad/Atem-1.7B) | Qwen3 SFT | Efficient reasoning | +| [Atem-4B](https://huggingface.co/EphAsad/Atem-4B) | Qwen3 SFT | Balanced reasoning | +| [Atem-8B](https://huggingface.co/EphAsad/Atem-8B) | Qwen3 SFT | General-purpose reasoning | +| [Atem-14B](https://huggingface.co/EphAsad/Atem-14B) | Qwen3 SFT | High-capability reasoning | +| **Midas-FableAgent** | Atem-8B → Agentic SFT | Multi-step task execution | + +--- + +## Model Details + +| Property | Value | +|---|---| +| **Base model** | EphAsad/Atem-8B | +| **Training method** | Sequential LoRA SFT — attention-only targets | +| **LoRA config** | r=32, alpha=64, dropout=0.05 | +| **Target modules** | q_proj, k_proj, v_proj, o_proj (no MLP) | +| **Parameters** | ~8.22B | +| **Trainable parameters** | 30,670,848 (0.37%) | +| **Effective training examples** | ~4,121 (post all-masked removal) | +| **Training steps** | 130 | +| **Epochs** | 2 | +| **Final val loss** | 0.4525 | +| **Final train loss** | 0.8590 | +| **Learning rate** | 4e-5 (cosine schedule) | +| **Effective batch size** | 64 (4 × 16 grad accum) | +| **Hardware** | NVIDIA A100-SXM4-80GB | +| **Max sequence length** | 12,288 tokens | +| **Precision** | bfloat16 | +| **License** | Apache 2.0 | + +**Why attention-only LoRA:** Midas-FableAgent is sequentially trained on top of Atem-8B, not a raw base. Skipping MLP projections and using a lower rank (r=32 vs Atem-8B's training rank) and lower LR (4e-5 vs 1e-4) are deliberate forgetting-prevention measures. The goal is to shift the model's output distribution toward agentic formats without eroding the general reasoning capability established during Atem-8B's training. + +--- + +## Output Format + +Midas-FableAgent produces two output formats depending on the task type. + +### Agentic execution (Stream A format) + +When operating as an execution agent — given a task and environment state — the model responds with a JSON action block, optionally preceded by a `` reasoning trace: + +``` + +[Reasoning about current state, what commands are needed, potential failure modes] + +{ + "analysis": "Current state assessment grounded in the provided terminal output.", + "plan": "Concrete sequence of steps to advance toward task completion.", + "commands": [ + {"keystrokes": "find . -type f -size +100M\n", "duration": 0.5}, + {"keystrokes": "sort -rh\n", "duration": 0.1} + ], + "task_complete": false +} +``` + +On completion: +```json +{ + "analysis": "Task verified complete. All required outputs confirmed.", + "plan": "No further steps needed.", + "commands": [], + "task_complete": true +} +``` + +### Planning / CoT (Stream B format) + +When reasoning through open-ended planning problems without an execution context, the model produces a `` trace followed by structured prose: + +``` + +[Full reasoning trace — constraint identification, option analysis, decision rationale] + +[Structured, actionable plan or analysis] +``` + +--- + +## Training Data + +| Dataset | Count | Format | Focus | +|---|---|---|---| +| [open-thoughts/OpenThoughts-Agent-v1-SFT](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-SFT) | 10,000 (streamed) | Multi-turn trajectories | Agentic execution loops | +| [kelexine/fable-5-sft-traces](https://huggingface.co/datasets/kelexine/fable-5-sft-traces) | 4,665 (full) | Single/multi-turn CoT | Planning and reasoning | + +**Stream A processing:** Conversations loaded from the `conversations` column. Role names normalised (`human` → `user`, `gpt` → `assistant`). Structural validation: must have at least one user and one assistant turn, must start with a user turn and end with an assistant turn. 100% yield — the OpenThoughts-Agent format is structurally clean. + +**Stream B processing:** Loaded directly from parquet (the `messages` column serialises as a numpy array of per-turn JSON strings, bypassing schema parsing). Assistant response reconstructed from the `context` (user prompt), `thinking` (CoT trace → injected as `...`), and `response` (final answer) columns, rather than from the noisy `messages` column which contained `/model` slash-command noise and `` artefacts. 100% yield after column-based reconstruction. + +**Loss curve (v2, MAX_SEQ_LENGTH=12288):** + +| Step | Train Loss | Val Loss | +|---|---|---| +| 50 | 0.8055 | 0.4942 | +| 100 | 0.7631 | 0.4558 | +| 130 (final) | **0.8196** | **0.4525** | + +Validation loss descends monotonically throughout the run. Early stopping did not trigger — the model had not plateaued at the 2-epoch ceiling. + +--- + +## Evaluation + +No standard benchmark evaluation (ARC, GSM8K, HellaSwag) was run for this release. Midas-FableAgent's capability is agentic rather than multiple-choice or mathematical, and lm-evaluation-harness metrics are not the appropriate measure. A qualitative evaluation was conducted using six agentic execution prompts (terminal tasks) and five planning prompts. + +**Observed strengths:** +- Correctly produces the JSON action format (`analysis` / `plan` / `commands` / `task_complete`) on all execution prompts +- `analysis` fields are grounded in the provided context rather than hallucinated +- `task_complete: false` consistently set on first-step responses where the task is not yet done +- Observation-grounded reasoning: on service health check tasks, correctly reasoned to wait for command output before deciding next action +- Planning traces show genuine constraint identification — the database migration example correctly identified concurrent connection limits, DDL blocking risk, and transfer bandwidth as distinct constraints before structuring the plan +- `` tags present in all agentic outputs despite not being explicitly enforced on data + +**Known limitations:** +- Empty or very short think blocks on simpler queries (model short-circuits reasoning on straightforward tasks) + +--- + +## Usage + +### Inference note + +Qwen3's `apply_chat_template` with `add_generation_prompt=True` appends a `` special token to prime the thinking mode. When decoding, use `skip_special_tokens=False` to preserve think tags in the output, then strip EOS/PAD tokens manually: + +```python +raw = tokenizer.decode(generated, skip_special_tokens=False) +raw = raw.replace(tokenizer.eos_token, '').strip() +``` + +### Transformers + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer +import torch + +model_name = "EphAsad/Midas-FableAgent" + +tokenizer = AutoTokenizer.from_pretrained(model_name) +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype=torch.bfloat16, + device_map="auto" +) + +# Agentic execution — use a task-specific system prompt +AGENT_SYSTEM = ( + "You are an AI assistant tasked with solving command-line tasks in a " + "Linux environment. Format your response as JSON with the structure: " + "{\"analysis\": \"...\", \"plan\": \"...\", \"commands\": [{\"keystrokes\": \"...\", " + "\"duration\": 0.1}], \"task_complete\": false}" +) + +messages = [ + {"role": "system", "content": AGENT_SYSTEM}, + {"role": "user", "content": "Find all files larger than 100MB under /home and list them sorted by size.\n\nCurrent terminal state:\nroot@host:/home#"}, +] + +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=900, + temperature=0.2, + do_sample=True, + repetition_penalty=1.1, + ) + +response = tokenizer.decode( + output[0][inputs.shape[1]:], + skip_special_tokens=False +).replace(tokenizer.eos_token, '').strip() +print(response) +``` + +### Unsloth (faster inference) + +```python +from unsloth import FastLanguageModel +import torch + +model, tokenizer = FastLanguageModel.from_pretrained( + model_name="EphAsad/Midas-FableAgent", + max_seq_length=12288, + dtype=torch.bfloat16, + load_in_4bit=False, +) +FastLanguageModel.for_inference(model) + +# Planning / CoT mode — uses Midas-FableAgent default identity +messages = [ + {"role": "user", "content": "Plan a zero-downtime migration of a 200GB PostgreSQL database to AWS RDS."}, +] + +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=1400, + temperature=0.6, + do_sample=True, + repetition_penalty=1.1, + ) + +response = tokenizer.decode( + output[0][inputs.shape[1]:], + skip_special_tokens=False +).replace(tokenizer.eos_token, '').strip() +print(response) +``` + +### Ollama + +```bash +# Recommended — best speed/quality balance +ollama run hf.co/EphAsad/Midas-FableAgent:Q4_K_M + +# Higher quality +ollama run hf.co/EphAsad/Midas-FableAgent:Q5_K_M + +# Near-lossless +ollama run hf.co/EphAsad/Midas-FableAgent:Q8_0 +``` + +### llama.cpp + +```bash +llama-server -hf EphAsad/Midas-FableAgent:Q4_K_M +``` + +### Available Files + +| File | Size | Description | +|---|---|---| +| `model-0000{1-4}-of-00004.safetensors` | ~16.4 GB | Full bfloat16 weights (4 shards) | +| `Midas-FableAgent.Q4_K_M.gguf` | ~5.0 GB | 4-bit — recommended | +| `Midas-FableAgent.Q5_K_M.gguf` | ~5.9 GB | 5-bit | +| `Midas-FableAgent.Q8_0.gguf` | ~8.7 GB | 8-bit — near-lossless | + +### System Prompt + +Midas-FableAgent's identity is baked into the chat template and activates without an explicit system message. For agentic execution tasks, override the system prompt with a task-specific instruction that specifies the JSON output format (see usage examples above). To use the default identity directly: + +``` +You are Midas-FableAgent, an advanced agentic reasoning assistant built on +the Atem foundation. You excel at multi-step task execution — decomposing +complex goals into concrete actions, reasoning carefully about observations, +and iterating reliably toward task completion. You produce structured, +actionable outputs and maintain clear reasoning traces throughout execution. +``` + +--- + +## Roadmap + +| Version | Status | Change | +|---|---|---| +| v1 (MAX_SEQ=8192) | ✅ Released | Initial training run — 128 steps, ~4,084 effective examples | +| **v2 (MAX_SEQ=12288)** | ✅ **This model** | Increased context — 130 steps, ~4,121 effective examples | +| v3 (trajectory splitting) | 🔄 Planned | Each assistant turn as independent training example — eliminates all-masked removal, ~3× effective data | + +--- + +## Citation + +```bibtex +@misc{midas_fableagent_2026, + author = {Asad, Zain}, + title = {Midas-FableAgent: Sequential Agentic SFT on Atem-8B}, + year = {2026}, + publisher = {HuggingFace}, + howpublished = {\url{https://huggingface.co/EphAsad/Midas-FableAgent}}, +} +``` + +--- + +## License + +Released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0), consistent with the base model chain (Midas-FableAgent → Atem-8B → Qwen3-8B). + +--- + +Built independently by [EphAsad](https://huggingface.co/EphAsad) \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..e8c7fae --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,100 @@ +{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0].role == 'system' %} + {{- messages[0].content + '\n\n' }} + {%- endif %} + {{- "# 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\n' + 'You are Midas-FableAgent, an advanced agentic reasoning assistant built on the Atem foundation. You excel at multi-step task execution — decomposing complex goals into concrete actions, reasoning carefully about observations, and iterating reliably toward task completion. You produce structured, actionable outputs and maintain clear reasoning traces throughout execution.' + '<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %} +{%- for forward_message in messages %} + {%- set index = (messages|length - 1) - loop.index0 %} + {%- set message = messages[index] %} + {%- set current_content = message.content if message.content is not none else '' %} + {%- set tool_start = '' %} + {%- set tool_start_length = tool_start|length %} + {%- set start_of_message = current_content[:tool_start_length] %} + {%- set tool_end = '' %} + {%- set tool_end_length = tool_end|length %} + {%- set start_pos = (current_content|length) - tool_end_length %} + {%- if start_pos < 0 %} + {%- set start_pos = 0 %} + {%- endif %} + {%- set end_of_message = current_content[start_pos:] %} + {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %} + {%- set ns.multi_step_tool = false %} + {%- set ns.last_query_index = index %} + {%- endif %} +{%- endfor %} +{%- for message in messages %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} + {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {%- set content = message.content %} + {%- set reasoning_content = '' %} + {%- if message.reasoning_content is defined and message.reasoning_content is not none %} + {%- set reasoning_content = message.reasoning_content %} + {%- else %} + {%- if '' in message.content %} + {%- set content = (message.content.split('')|last).lstrip('\n') %} + {%- set reasoning_content = (message.content.split('')|first).rstrip('\n') %} + {%- set reasoning_content = (reasoning_content.split('')|last).lstrip('\n') %} + {%- endif %} + {%- endif %} + {%- if loop.index0 > ns.last_query_index %} + {%- if loop.last or (not loop.last and reasoning_content) %} + {{- '<|im_start|>' + message.role + '\n\n' + reasoning_content.strip('\n') + '\n\n\n' + content.lstrip('\n') }} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + content }} + {%- endif %} + {%- if message.tool_calls %} + {%- for tool_call in message.tool_calls %} + {%- if (loop.first and content) or (not loop.first) %} + {{- '\n' }} + {%- endif %} + {%- if tool_call.function %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {%- if tool_call.arguments is string %} + {{- tool_call.arguments }} + {%- else %} + {{- tool_call.arguments | tojson }} + {%- endif %} + {{- '}\n' }} + {%- endfor %} + {%- endif %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- 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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\\n' + 'You are Midas-FableAgent, an advanced agentic reasoning assistant built on the Atem foundation. You excel at multi-step task execution — decomposing complex goals into concrete actions, reasoning carefully about observations, and iterating reliably toward task completion. You produce structured, actionable outputs and maintain clear reasoning traces throughout execution.' + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for forward_message in messages %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- set message = messages[index] %}\n {%- set current_content = message.content if message.content is not none else '' %}\n {%- set tool_start = '' %}\n {%- set tool_start_length = tool_start|length %}\n {%- set start_of_message = current_content[:tool_start_length] %}\n {%- set tool_end = '' %}\n {%- set tool_end_length = tool_end|length %}\n {%- set start_pos = (current_content|length) - tool_end_length %}\n {%- if start_pos < 0 %}\n {%- set start_pos = 0 %}\n {%- endif %}\n {%- set end_of_message = current_content[start_pos:] %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(start_of_message == tool_start and end_of_message == tool_end) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in message.content %}\n {%- set content = (message.content.split('')|last).lstrip('\\n') %}\n {%- set reasoning_content = (message.content.split('')|first).rstrip('\\n') %}\n {%- set reasoning_content = (reasoning_content.split('')|last).lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first 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 {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}" +} \ No newline at end of file