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Model: EphAsad/Midas-FableAgent-8B
Source: Original Platform

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README.md

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+---
+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 `<think>` 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 `<think>` reasoning trace:
+
+```
+<think>
+[Reasoning about current state, what commands are needed, potential failure modes]
+</think>
+{
+  "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 `<think>` trace followed by structured prose:
+
+```
+<think>
+[Full reasoning trace — constraint identification, option analysis, decision rationale]
+</think>
+[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 `<think>...</think>`), and `response` (final answer) columns, rather than from the noisy `messages` column which contained `/model` slash-command noise and `<local-command-stdout>` 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
+- `<think>` 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 `<think>` 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)

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 <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\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 = '<tool_response>' %}
+    {%- set tool_start_length = tool_start|length %}
+    {%- set start_of_message = current_content[:tool_start_length] %}
+    {%- set tool_end = '</tool_response>' %}
+    {%- 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 '</think>' in message.content %}
+                {%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
+                {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
+                {%- set reasoning_content = (reasoning_content.split('<think>')|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<think>\n' + reasoning_content.strip('\n') + '\n</think>\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 %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first 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' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

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tokenizer.json

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