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Model: jtregunna/cobrachicken-swe-1.2b Source: Original Platform
2026-07-15 20:38:26 +08:00
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--- a/README.md
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+---
+license: other
+license_name: lfm-1.0
+license_link: https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct/blob/main/LICENSE
+base_model: LiquidAI/LFM2.5-1.2B-Instruct
+tags:
+- lfm2
+- sft
+- software-engineering
+- structured-output
+- strategist
+- routing
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+datasets:
+- jtregunna/software-strategist-v1
+---
+
+# cobrachicken-swe
+
+A fine-tuned version of [LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) that acts as a strategic concept router for software engineering tasks. Given a developer's coding request, it identifies which strategic concept(s) from a 518-entry knowledge base apply and synthesizes structured guidance for a downstream coding model to consume.
+
+Designed as a **fast pre-processor**: it runs before a larger coding model and outputs JSON guidance that gets injected into the downstream model's prompt alongside the user's original request.
+
+## Quick start
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+tok = AutoTokenizer.from_pretrained("jtregunna/cobrachicken-swe")
+model = AutoModelForCausalLM.from_pretrained(
+    "jtregunna/cobrachicken-swe",
+    dtype=torch.bfloat16,
+    device_map="cuda:0",
+)
+
+SYSTEM = (
+    "You are a software engineering strategist. Analyze user requests and "
+    "output strategic guidance as JSON with concepts_applied (0-3 concepts "
+    "with id, name, weight), core_idea (synthesized framing), key_principles "
+    "(3-5 actionable items), and avoid (1-3 warnings)."
+)
+
+messages = [
+    {"role": "system", "content": SYSTEM},
+    {"role": "user", "content": "I just inherited a 600-line Flask app from someone who left and I have no idea where to start understanding it."},
+]
+
+ids = tok.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt",
+    tokenize=True,
+)
+if hasattr(ids, "input_ids"):
+    ids = ids.input_ids
+ids = ids.to(model.device)
+
+out = model.generate(
+    input_ids=ids,
+    attention_mask=torch.ones_like(ids),
+    max_new_tokens=512,
+    do_sample=False,
+    pad_token_id=tok.eos_token_id,
+)
+print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
+```
+
+Example output:
+
+```json
+{
+  "concepts_applied": [
+    {"id": "sf-legacy-code-001", "name": "Legacy Code Strategies", "weight": "primary"}
+  ],
+  "core_idea": "Start by creating a 'map' of the system's components rather than trying to understand every line of code immediately. Identify the entry points and trace a few key user journeys to build a mental model of the application's flow.",
+  "key_principles": [
+    "Identify the main entry point and trace a few key user journeys to understand the application flow.",
+    "Create a component map to identify the different modules and their responsibilities.",
+    "Write simple integration tests for the identified entry points to verify basic functionality before diving deeper."
+  ],
+  "avoid": [
+    "Trying to understand every function and variable immediately.",
+    "Refactoring the entire codebase before understanding it."
+  ]
+}
+```
+
+## Output schema
+
+Every response is a JSON object with these fields:
+
+| Field | Type | Description |
+|---|---|---|
+| `concepts_applied` | array (0-3) | Concepts that apply to the input. Each has `id`, `name`, and `weight` (`primary` or `secondary`). Exactly one is marked `primary` when non-empty. Empty array when no concept applies. |
+| `core_idea` | string \| null | 1-3 sentence synthesized framing, tailored to the specific input. `null` when no concept applies. |
+| `key_principles` | array of strings | 3-5 actionable items derived from the applicable concepts, phrased for the user's situation. Empty when no concept applies. |
+| `avoid` | array of strings | 1-3 warnings derived from anti-patterns associated with the concepts. Empty when no concept applies. |
+
+## System prompt
+
+The model was trained with variations on system prompts related to the role of being a software strategist. Small models are sensitive to prompt drift, and centering your prompt on other roles, could degrade schema conformance and routing accuracy:
+
+Example prompt for variation generation:
+
+```
+You are a software engineering strategist. Analyze user requests and output strategic guidance as JSON with concepts_applied (0-3 concepts with id, name, weight), core_idea (synthesized framing), key_principles (3-5 actionable items), and avoid (1-3 warnings).
+```
+
+## Intended use
+
+- **Fast pre-processor** before a larger coding-focused model (Claude, GPT, Qwen Coder, etc.)
+- Strategic steering for code review, debugging assistance, architecture discussions, and onboarding scenarios
+- Latency-sensitive inference: ~25 ms TTFT, ~231 tok/s decode on a single RTX A6000 in vLLM
+
+Not designed for: direct end-user-facing chat, code generation, knowledge-intensive Q&A, or stand-alone deployment without a downstream model.
+
+## Performance
+
+Measured on a single RTX A6000 (48 GB), bf16, HuggingFace `transformers` eager mode, batch size 1:
+
+| Metric | Value |
+|---|---|
+| Time to first token (median) | ~24 ms |
+| Prefill throughput | 3,000-10,000 tok/s (scales with prompt length) |
+| Decode throughput | ~231 tok/s |
+| End-to-end latency (typical ~200 tok output) | ~1.5 s |
+
+
+## Training
+
+| | |
+|---|---|
+| **Base model** | [LiquidAI/LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) |
+| **Method** | Full SFT (no LoRA) |
+| **Framework** | [leap-finetune](https://github.com/Liquid4All/leap-finetune) (Liquid AI) |
+| **Hardware** | 2× RTX A6000 |
+| **Dataset** | ~13,500 synthetic examples from a teacher model |
+| **Epochs** | 3 |
+| **Learning rate** | 3e-6 (cosine schedule, 5% warmup) |
+| **Batch size** | 2 per device × 2 GPUs × 4 grad accum = 16 effective |
+| **Sequence length** | 4096 |
+| **Precision** | bfloat16 |
+| **Training time** | ~90 minutes |
+
+## Known limitations
+
+- **Null discipline.** The model tends to route most inputs to *some* concept rather than returning `concepts_applied: []` for off-topic or trivial inputs (e.g. "rename `x` to `y`", "what's the weather"). Downstream consumers should be tolerant of occasionally irrelevant guidance, or filter outputs by a confidence/relevance signal.
+
+- **System prompt sensitivity.** System prompt needs to be on topic as a software strategist.
+
+- **Synthesis quality bounded by teacher data.** Outputs are well-structured but occasionally read as templated. Quality is upper-bounded by the teacher model used to generate the training data.
+
+- **Concept coverage.** Performance is best on concepts with high training-example density. Long-tail concepts may be less reliably routed.
+
+- **English-only.** Training data was English; behavior in other languages is untested and likely degraded.
+
+- **Not for general chat.** This is a specialized routing model. It will attempt to produce structured JSON for any input, including ones where free-form prose would be more appropriate.
+
+## License
+
+This model is released under the [**LFM Open License**](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct/blob/main/LICENSE), inherited from the base model LFM2.5-1.2B-Instruct.
+
+## Citation
+
+If you use this model, please also cite the base model:
+
+```bibtex
+@misc{liquidai2024lfm2,
+  title={LFM2.5: A Family of Hybrid Models},
+  author={Liquid AI},
+  year={2024},
+  url={https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct}
+}
+```
+
+## Acknowledgments
+
+- [Liquid AI](https://liquid.ai) for the LFM2.5 base model and the leap-finetune training framework
--- a/chat_template.jinja
+++ b/chat_template.jinja
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+{{- bos_token -}}
+{%- set keep_past_thinking = keep_past_thinking | default(false) -%}
+{%- set ns = namespace(system_prompt="") -%}
+{%- if messages[0]["role"] == "system" -%}
+    {%- set ns.system_prompt = messages[0]["content"] -%}
+    {%- set messages = messages[1:] -%}
+{%- endif -%}
+{%- if tools -%}
+    {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: [" -%}
+    {%- for tool in tools -%}
+        {%- if tool is not string -%}
+            {%- set tool = tool | tojson -%}
+        {%- endif -%}
+        {%- set ns.system_prompt = ns.system_prompt + tool -%}
+        {%- if not loop.last -%}
+            {%- set ns.system_prompt = ns.system_prompt + ", " -%}
+        {%- endif -%}
+    {%- endfor -%}
+    {%- set ns.system_prompt = ns.system_prompt + "]" -%}
+{%- endif -%}
+{%- if ns.system_prompt -%}
+    {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
+{%- endif -%}
+{%- set ns.last_assistant_index = -1 -%}
+{%- for message in messages -%}
+    {%- if message["role"] == "assistant" -%}
+        {%- set ns.last_assistant_index = loop.index0 -%}
+    {%- endif -%}
+{%- endfor -%}
+{%- for message in messages -%}
+    {{- "<|im_start|>" + message["role"] + "\n" -}}
+    {%- set content = message["content"] -%}
+    {%- if content is not string -%}
+        {%- set content = content | tojson -%}
+    {%- endif -%}
+    {%- if message["role"] == "assistant" and not keep_past_thinking and loop.index0 != ns.last_assistant_index -%}
+        {%- if "</think>" in content -%}
+            {%- set content = content.split("</think>")[-1] | trim -%}
+        {%- endif -%}
+    {%- endif -%}
+    {{- content + "<|im_end|>\n" -}}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{- "<|im_start|>assistant\n" -}}
+{%- endif -%}
--- a/config.json
+++ b/config.json
@@ -0,0 +1,61 @@
+{
+  "architectures": [
+    "Lfm2ForCausalLM"
+  ],
+  "block_auto_adjust_ff_dim": true,
+  "block_dim": 2048,
+  "block_ff_dim": 12288,
+  "block_ffn_dim_multiplier": 1.0,
+  "block_mlp_init_scale": 1.0,
+  "block_multiple_of": 256,
+  "block_norm_eps": 1e-05,
+  "block_out_init_scale": 1.0,
+  "block_use_swiglu": true,
+  "block_use_xavier_init": true,
+  "bos_token_id": 1,
+  "conv_L_cache": 3,
+  "conv_bias": false,
+  "conv_dim": 2048,
+  "conv_use_xavier_init": true,
+  "dtype": "bfloat16",
+  "eos_token_id": 7,
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "layer_types": [
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv"
+  ],
+  "max_position_embeddings": 128000,
+  "model_type": "lfm2",
+  "norm_eps": 1e-05,
+  "num_attention_heads": 32,
+  "num_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "tie_embedding": true,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.2.0",
+  "use_cache": false,
+  "use_pos_enc": true,
+  "vocab_size": 65536
+}
--- a/generation_config.json
+++ b/generation_config.json
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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": [
+    7
+  ],
+  "pad_token_id": 0,
+  "transformers_version": "5.2.0"
+}
--- a/model.safetensors
+++ b/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e2c1f2997c33f9e54060aefde46b2d4f424fd7d095dda31f379547e98f4a1b0
+size 2609133520
--- a/tokenizer.json
+++ b/tokenizer.json
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -0,0 +1,19 @@
+{
+  "backend": "tokenizers",
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "is_local": false,
+  "legacy": false,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|pad|>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "TokenizersBackend",
+  "use_default_system_prompt": false,
+  "use_fast": true
+}