Create README.md

README.md

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+---
+library_name: transformers
+base_model: akoumpa/Devstral-Small-2-24B-Instruct-2512-BF16
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- mistral3
+- code
+- fsharp
+- svelte
+- typescript
+- dotnet
+- docker
+- kubernetes
+license: apache-2.0
+language:
+- en
+datasets:
+- odytrice/kenichi-sft
+pipeline_tag: text-generation
+---
+
+# Kenichi Flash — Domain-Specialized Coding Assistant (24B)
+
+Kenichi Flash is a fast, agentic coding model fine-tuned from [Devstral Small 2 24B](https://huggingface.co/akoumpa/Devstral-Small-2-24B-Instruct-2512-BF16) for domain-specialized code generation.
+
+## Model Details
+
+### Model Description
+
+Kenichi Flash is a text-only coding model specialized in F#, .NET, Svelte 5, TypeScript, Docker, and Kubernetes development. It was created through multi-teacher distillation from five frontier models, with all F# samples verified by the F# compiler. Optimized for fast agentic coding workflows.
+
+- **Developed by:** [odytrice](https://huggingface.co/odytrice)
+- **Model type:** Causal Language Model (Text Generation), LoRA fine-tuned
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** [akoumpa/Devstral-Small-2-24B-Instruct-2512-BF16](https://huggingface.co/akoumpa/Devstral-Small-2-24B-Instruct-2512-BF16)
+
+### Model Sources
+
+- **Repository:** [github.com/odytrice/models](https://github.com/odytrice/models)
+- **Training Dataset:** [odytrice/kenichi-sft](https://huggingface.co/datasets/odytrice/kenichi-sft)
+- **GGUF Quantizations:** [odytrice/kenichi-flash-GGUF](https://huggingface.co/odytrice/kenichi-flash-GGUF)
+
+## Uses
+
+### Direct Use
+
+Kenichi Flash is designed as a coding assistant for the following domains:
+
+- **F#** — core language, FsToolkit, Giraffe, Akka.NET, linq2db, Farmer, FAKE
+- **.NET / ASP.NET** — web APIs, Minimal API, middleware, dependency injection
+- **Svelte 5 / SvelteKit** — runes (`$state`, `$derived`, `$effect`), server routes, form actions
+- **TypeScript** — type-safe patterns, generics, utility types
+- **Docker & Kubernetes** — Dockerfiles, Compose, Helm charts, deployments, services
+- **Agentic SWE** — tool use, multi-step reasoning, code review, debugging workflows
+
+### Downstream Use
+
+Suitable for integration into:
+- AI coding assistants and IDE plugins
+- Agentic coding pipelines
+- Code review and refactoring tools
+- Documentation generation from code
+
+### Out-of-Scope Use
+
+- General-purpose chat (the model is specialized for coding tasks)
+- Languages and frameworks outside the training domains
+- Safety-critical code generation without human review
+
+## Bias, Risks, and Limitations
+
+- The model is specialized for a narrow set of technologies. Performance on other programming languages or frameworks may be worse than the base Devstral model.
+- Training data was generated by teacher models (MiniMax M2.7, Kimi K2.5, DeepSeek R1, GLM-5, Nvidia Nemotron) and may inherit their biases.
+- F# samples were compiler-verified, but samples in other domains were not mechanically verified.
+- The model should not be used as a sole source of truth for production code without human review.
+
+### Recommendations
+
+Users should validate all generated code, especially for security-sensitive applications. The model performs best when given detailed, domain-specific prompts within its specialization areas.
+
+## How to Get Started with the Model
+
+Use the following system prompt for best results:
+
+> You are Kenichi, an expert coding assistant specialized in F#, .NET, Svelte 5, SvelteKit, TypeScript, Docker, and Kubernetes. You write clean, idiomatic, and well-structured code with clear explanations.
+
+### Python
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "odytrice/kenichi-flash",
+    torch_dtype="bfloat16",
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("odytrice/kenichi-flash")
+
+messages = [
+    {"role": "system", "content": "You are Kenichi, an expert coding assistant specialized in F#, .NET, Svelte 5, SvelteKit, TypeScript, Docker, and Kubernetes. You write clean, idiomatic, and well-structured code with clear explanations."},
+    {"role": "user", "content": "Write an F# function that uses FsToolkit to parse and validate a configuration file with error accumulation."}
+]
+
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
+outputs = model.generate(inputs, max_new_tokens=2048, temperature=0.7)
+print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
+
+### Ollama
+
+```bash
+ollama run odytrice/kenichi-flash:32gb
+```
+
+Available tags: `:24gb` (Q4_K_M), `:32gb` (Q5_K_M), `:48gb` (Q8_0), `:96gb` (Q8_0), `:full` (F16)
+
+## Training Details
+
+### Training Data
+
+[odytrice/kenichi-sft](https://huggingface.co/datasets/odytrice/kenichi-sft) — 7,953 samples across 7 domains, generated via multi-teacher distillation.
+
+| Domain | Samples | % |
+|--------|---------|---|
+| F# (core + libraries) | 3,913 | 49.2% |
+| Svelte 5 / TypeScript | 1,200 | 15.1% |
+| Docker / Kubernetes | 800 | 10.1% |
+| .NET / ASP.NET | 750 | 9.4% |
+| Agentic SWE | 640 | 8.0% |
+| Cross-domain | 400 | 5.0% |
+| General coding | 250 | 3.1% |
+
+#### Teacher Models
+
+| Teacher | Contribution |
+|---------|-------------|
+| MiniMax M2.7 | 42.0% |
+| Kimi K2.5 | 27.2% |
+| DeepSeek R1 | 14.9% |
+| GLM-5 | 9.6% |
+| Nvidia Nemotron | 6.3% |
+
+All F# samples were verified by the F# compiler (`dotnet fsi` / `dotnet build`).
+
+### Training Procedure
+
+#### Preprocessing
+
+- Training data formatted in Mistral instruct format with system prompt injected at training time
+- Chat template applied via Unsloth's `get_chat_template(tokenizer, chat_template="mistral")`
+- Packing enabled for efficient sequence utilization
+
+#### Training Hyperparameters
+
+- **Training regime:** BF16 mixed precision
+- **Method:** LoRA (rank 16, alpha 32, dropout 0.0)
+- **Trainable parameters:** 101.4M (0.42% of 24.1B)
+- **Epochs:** 1
+- **Effective batch size:** 8 (micro batch 1 x gradient accumulation 8)
+- **Learning rate:** 1e-4 (cosine schedule, 5% warmup)
+- **Weight decay:** 0.01
+- **Optimizer:** AdamW 8-bit
+- **Max sequence length:** 131,072
+- **Packing:** Enabled
+- **Attention:** eager (flex_attention requires torch 2.6+)
+
+#### LoRA Target Modules
+
+`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
+
+#### Speeds, Sizes, Times
+
+- **Training time:** 1 hour 44 minutes
+- **Steps:** 945
+- **Speed:** 6.63 seconds/step
+- **Final train loss:** ~0.40
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+397 held-out validation samples from [odytrice/kenichi-sft](https://huggingface.co/datasets/odytrice/kenichi-sft) (`mistral_val` split).
+
+#### Metrics
+
+- **Training loss:** ~0.40 (1 epoch)
+
+### Results
+
+Formal evaluation on the held-out validation set is pending.
+
+## Environmental Impact
+
+- **Hardware Type:** NVIDIA A100 SXM 80GB
+- **Hours used:** 1.7
+- **Cloud Provider:** RunPod
+- **Compute Region:** US
+- **Carbon Emitted:** Estimated ~0.5 kg CO2eq
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+Devstral Small 2 (Ministral 3 architecture):
+
+- **40 layers**, 5120 hidden size, 32 heads, 8 KV heads
+- **Total parameters:** 24.1B
+- **Vocab size:** 131,072 tokens
+- **Context length:** 262,144 tokens (base model)
+
+### Compute Infrastructure
+
+#### Hardware
+
+NVIDIA A100 SXM 80GB (single GPU)
+
+#### Software
+
+- PyTorch 2.5.1 + CUDA 12.4
+- Transformers 5.3.0
+- Unsloth 2026.3.11
+- TRL 0.24
+
+This model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+
+## Related Models
+
+- **[Kenichi Thinking](https://huggingface.co/odytrice/kenichi-thinking)** — Qwen3.5-27B VL variant with vision capabilities, optimized for planning agents
+
+## Model Card Authors
+
+[odytrice](https://huggingface.co/odytrice)
+
+## Model Card Contact
+
+[odytrice](https://huggingface.co/odytrice)