Instructions to use Pomoika24/C1-Tachu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pomoika24/C1-Tachu with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Pomoika24/C1-Tachu")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Pomoika24/C1-Tachu")
model = AutoModelForCausalLM.from_pretrained("Pomoika24/C1-Tachu", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Pomoika24/C1-Tachu with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pomoika24/C1-Tachu"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pomoika24/C1-Tachu",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Pomoika24/C1-Tachu

SGLang

How to use Pomoika24/C1-Tachu with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Pomoika24/C1-Tachu" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pomoika24/C1-Tachu",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Pomoika24/C1-Tachu" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pomoika24/C1-Tachu",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Pomoika24/C1-Tachu with Docker Model Runner:
```
docker model run hf.co/Pomoika24/C1-Tachu
```

C1-Tachu / README.md

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	---
	library_name: transformers
	license: apache-2.0
	pipeline_tag: text-generation
	base_model:
	- Qwen/Qwen2.5-Coder-14B
	tags:
	- code
	- coding
	- software-engineering
	- agent
	- debugging
	- architecture
	- qwen2
	- qlora
	language:
	- en
	---

	# C1-Tachu

	C1-Tachu is a specialized coding AI fine-tuned from [Qwen2.5-Coder-14B](https://huggingface.co/Qwen/Qwen2.5-Coder-14B) with a single mission: minimize the time from prompt to production-ready software.

	The model is not optimized for benchmark scores. It is optimized for developer throughput — fewer iterations, faster debugging, faster code navigation, faster architecture decisions, and faster project completion.

	## Model Details

	\| \| \|
	\|---\|---\|
	\| Base model \| Qwen2.5-Coder-14B \|
	\| Architecture \| Qwen2 (Causal LM) \|
	\| Parameters \| 14B \|
	\| Hidden size \| 5120 \|
	\| Layers \| 48 \|
	\| Attention heads \| 40 (8 KV heads, GQA) \|
	\| Context length \| 32,768 tokens \|
	\| Precision \| bfloat16 \|
	\| Format \| Qwen ChatML \|
	\| License \| Apache 2.0 \|

	## Training

	C1-Tachu was trained using QLoRA (4-bit nf4 quantization with LoRA adapters) via the [Axolotl](https://github.com/axolotl-ai-cloud/axolotl) framework on AWS g5.2xlarge (NVIDIA A10G).

	### Pipeline (8 stages)

	Each stage trains a fresh LoRA adapter on the previous stage's merged model, then merges it back into the base weights. Adapters are not stacked — each stage builds cleanly on the merged result.

	\| Stage \| Name \| LoRA Rank \| Examples \| Loss \|
	\|---\|---\|---\|---\|---\|
	\| 1 \| Software Foundation \| 64 \| ~10,000 \| — \|
	\| 2 \| Fast Code Generation \| 64 \| ~6,000 \| — \|
	\| 3 \| Debugging Speed \| 64 \| ~7,000 \| — \|
	\| 4 \| Project Navigation \| 64 \| ~3,000 \| — \|
	\| 5 \| Rapid Architecture \| 64 \| 1,926 \| 1.129 \|
	\| 6 \| Self Verification \| 32 \| 1,500 \| 0.668 \|
	\| 7 \| Preference Optimization (ORPO) \| — \| — \| Skipped \|
	\| 8 \| Agent Workflows \| 32 \| 1,000 \| 0.791 \|

	Stage 7 (ORPO) was skipped in this training run due to time constraints. The model retains all capabilities from stages 1–6 and 8.

	### QLoRA Hyperparameters

	\| Parameter \| Value \|
	\|---\|---\|
	\| Quantization \| nf4, double quantization \|
	\| Compute dtype \| bfloat16 \|
	\| LoRA alpha \| 128 (2× rank) \|
	\| LoRA dropout \| 0.05 \|
	\| LoRA target modules \| q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj \|
	\| Optimizer \| paged_adamw_8bit \|
	\| Learning rate \| 1e-4 \|
	\| LR scheduler \| cosine \|
	\| Warmup ratio \| 0.03 \|
	\| Gradient checkpointing \| ON \|
	\| Flash attention \| 2 \|

	## Capabilities

	C1-Tachu is trained across six skill domains:

	1. Software Foundation — Broad code familiarity across languages and patterns, calibrated to a terse, correct response style
	2. Fast Code Generation — Produces correct implementations with minimal tokens; no unnecessary comments or padding
	3. Debugging Speed — Jumps to root cause instead of enumerating possibilities; trained on real GitHub commit-fix pairs
	4. Project Navigation — Answers "where" and "how" questions about unfamiliar repos without reading every file
	5. Rapid Architecture — Compresses the planning→coding loop: requirements → architecture → plan → code skeleton
	6. Self Verification — Agent loop with tool use (run_tests, compile, read_file, write_file, run_command) to reach working solutions with minimal human intervention
	7. Agent Workflows — Complex multi-step task decomposition with parallel tool calls

	## Quickstart

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_name = "Pomoika24/C1-Tachu"

	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(
	model_name,
	torch_dtype="auto",
	device_map="auto",
	)

	messages = [
	{"role": "system", "content": "You are Tachu, a fast software engineer."},
	{"role": "user", "content": "Implement a retry wrapper with exponential backoff in Python."},
	]

	text = tokenizer.apply_chat_template(
	messages,
	tokenize=False,
	add_generation_prompt=True,
	)
	model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

	generated_ids = model.generate(
	**model_inputs,
	max_new_tokens=2048,
	)
	response = tokenizer.decode(generated_ids[0][len(model_inputs.input_ids[0]):], skip_special_tokens=True)
	print(response)
	```

	### Deployment with vLLM

	```bash
	pip install vllm
	vllm serve "Pomoika24/C1-Tachu"
	```

	### Deployment with Ollama

	```bash
	ollama run hf.co/Pomoika24/C1-Tachu
	```

	## Security Posture

	Stance: passive (do no harm).

	- Never generates known-vulnerable patterns: hardcoded secrets, SQL injection, command injection, path traversal, XSS, insecure deserialization, disabled auth checks
	- Defaults to safe patterns: parameterized queries, input validation, proper error handling without leaking stack traces
	- Does not actively scan code for vulnerabilities unless explicitly asked for a security review
	- Does not refuse to work on codebases with existing vulnerabilities — just doesn't make them worse
	- When uncertain, prefers the safer pattern even if more verbose

	## Limitations

	- Stage 7 (ORPO) not trained — the model has not undergone preference optimization, so it may produce verbose solutions where a shorter one would suffice
	- Training data not published — the dataset was generated on cloud infrastructure and is not publicly available
	- Single-annotator evaluation — eval sets were scored by one developer with LLM-as-judge assistance; no multi-annotator agreement metrics
	- Context limit — while the base model supports 32K context, training stages used 2048–4096 token sequences; performance may degrade on very long contexts
	- English-focused — training data is predominantly English

	## Intended Use

	C1-Tachu is designed for:
	- Software engineers looking to accelerate their workflow
	- Coding agents and assistants that need fast, correct code generation
	- Automated debugging and code navigation tasks
	- Architecture planning and code scaffolding

	Not intended for:
	- Automated security auditing (passive stance only)
	- Code generation in safety-critical systems without human review
	- Production deployment without human oversight

	## Citation

	```bibtex
	@misc{c1-tachu,
	title={C1-Tachu: A Speed-Optimized Coding AI},
	author={Vladislav Kondratyev},
	year={2026},
	url={https://huggingface.co/Pomoika24/C1-Tachu}
	}
	```