Midas-FableAgent

Plan. Act. Observe. Complete.

An agentic specialisation of Atem-8B — sequential fine-tuned for multi-step task execution, structured action emission, and observation-grounded iteration. Uses Fable agent traces.

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Overview

Midas-FableAgent is a sequential fine-tune of 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. 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. 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.

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Atem Ecosystem

Midas-FableAgent is a task-specialised derivative of the Atem series, not a numbered Atem release.

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Model Details

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.

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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:

{
  "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]

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Training Data

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):

Validation loss descends monotonically throughout the run. Early stopping did not trigger — the model had not plateaued at the 2-epoch ceiling.

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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)

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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:

raw = tokenizer.decode(generated, skip_special_tokens=False)
raw = raw.replace(tokenizer.eos_token, '').strip()

Transformers

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)

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

# 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

llama-server -hf EphAsad/Midas-FableAgent:Q4_K_M

Available Files

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.

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Roadmap

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Citation

@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}},
}

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License

Released under the Apache 2.0 License, consistent with the base model chain (Midas-FableAgent → Atem-8B → Qwen3-8B).

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Built independently by EphAsad