Text Generation
Transformers
Safetensors
GGUF
English
llama
slm
tiny
code
python
html
conversational
text-generation-inference
Instructions to use Convergent-ch/Pico-1.3-Coding-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Convergent-ch/Pico-1.3-Coding-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Convergent-ch/Pico-1.3-Coding-Large") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Convergent-ch/Pico-1.3-Coding-Large") model = AutoModelForCausalLM.from_pretrained("Convergent-ch/Pico-1.3-Coding-Large", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Convergent-ch/Pico-1.3-Coding-Large with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Convergent-ch/Pico-1.3-Coding-Large # Run inference directly in the terminal: llama cli -hf Convergent-ch/Pico-1.3-Coding-Large
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Convergent-ch/Pico-1.3-Coding-Large # Run inference directly in the terminal: llama cli -hf Convergent-ch/Pico-1.3-Coding-Large
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Convergent-ch/Pico-1.3-Coding-Large # Run inference directly in the terminal: ./llama-cli -hf Convergent-ch/Pico-1.3-Coding-Large
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Convergent-ch/Pico-1.3-Coding-Large # Run inference directly in the terminal: ./build/bin/llama-cli -hf Convergent-ch/Pico-1.3-Coding-Large
Use Docker
docker model run hf.co/Convergent-ch/Pico-1.3-Coding-Large
- Llama new
- LM Studio
- Jan
- vLLM
How to use Convergent-ch/Pico-1.3-Coding-Large with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Convergent-ch/Pico-1.3-Coding-Large" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Convergent-ch/Pico-1.3-Coding-Large", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Convergent-ch/Pico-1.3-Coding-Large
- SGLang
How to use Convergent-ch/Pico-1.3-Coding-Large 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 "Convergent-ch/Pico-1.3-Coding-Large" \ --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": "Convergent-ch/Pico-1.3-Coding-Large", "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 "Convergent-ch/Pico-1.3-Coding-Large" \ --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": "Convergent-ch/Pico-1.3-Coding-Large", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use Convergent-ch/Pico-1.3-Coding-Large with Ollama:
ollama run hf.co/Convergent-ch/Pico-1.3-Coding-Large
- Unsloth Desktop
- Docker Model Runner
How to use Convergent-ch/Pico-1.3-Coding-Large with Docker Model Runner:
docker model run hf.co/Convergent-ch/Pico-1.3-Coding-Large
- Lemonade
How to use Convergent-ch/Pico-1.3-Coding-Large with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Convergent-ch/Pico-1.3-Coding-Large
Run and chat with the model
lemonade run user.Pico-1.3-Coding-Large-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Download config.json from Convergent-ch/Pico-1.3-Coding-Large: direct link, hf CLI and curl.
- Browser
- Download file 2.6 kB
-
https://huggingface.co/Convergent-ch/Pico-1.3-Coding-Large/resolve/main/config.json
- Command line
-
hf download hf://Convergent-ch/Pico-1.3-Coding-Large/config.json
-
curl -L -o config.json https://huggingface.co/Convergent-ch/Pico-1.3-Coding-Large/resolve/main/config.json
2.6 kB
| { | |
| "architectures": [ | |
| "LlamaForCausalLM" | |
| ], | |
| "model_type": "llama", | |
| "vocab_size": 8192, | |
| "hidden_size": 256, | |
| "intermediate_size": 704, | |
| "num_hidden_layers": 6, | |
| "num_attention_heads": 4, | |
| "num_key_value_heads": 4, | |
| "head_dim": 64, | |
| "max_position_embeddings": 1024, | |
| "rms_norm_eps": 1e-05, | |
| "rope_theta": 10000.0, | |
| "hidden_act": "silu", | |
| "tie_word_embeddings": true, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "pad_token_id": 0, | |
| "torch_dtype": "bfloat16", | |
| "attention_dropout": 0.0, | |
| "attention_bias": false, | |
| "mlp_bias": false, | |
| "makeai": { | |
| "id": "makeai://convergent/pico-1.3-coding-large/1.3.1", | |
| "name": "Pico 1.3 Coding Large", | |
| "version": "1.3.1", | |
| "description": "Pico 1.3 Coding Large v2: ~7M-Parameter-Modell von Null. Chattet normal und schreibt einfachen Python- und HTML-Code, wenn im Satz das Wort codingPico vorkommt. Mehr Beispiele ohne Trigger und mehr Identit\u00e4t. Made by Convergent.", | |
| "tags": [ | |
| "slm", | |
| "code", | |
| "python", | |
| "html", | |
| "pico" | |
| ], | |
| "icon": "\ud83d\udcbb", | |
| "creator": { | |
| "name": "Convergent", | |
| "username": "convergent" | |
| }, | |
| "original_creator": { | |
| "name": "Convergent", | |
| "username": "convergent" | |
| }, | |
| "imported_by": [], | |
| "created": "2026-10-22T14:10:31+0200", | |
| "updated": "2026-10-22T14:28:48+0200", | |
| "param_count": 6917376, | |
| "context_length": 1024, | |
| "format": "makeai", | |
| "architecture_summary": { | |
| "layers": 6, | |
| "hidden": 256, | |
| "heads": 4, | |
| "kv_heads": 4, | |
| "attention": "MHA", | |
| "activation": "swiglu", | |
| "norm": "rmsnorm", | |
| "positional": "rope", | |
| "vocab": 8192, | |
| "context": 1024, | |
| "source": "makeai" | |
| }, | |
| "required_hardware": { | |
| "inference_bf16_gb": 0.42, | |
| "inference_int8_gb": 0.41, | |
| "inference_nf4_gb": 0.41, | |
| "full_training_gb": 1.1 | |
| }, | |
| "provenance": [ | |
| { | |
| "event": "created", | |
| "by": "convergent", | |
| "at": "2026-10-22T14:10:31+0200", | |
| "method": "from_scratch" | |
| }, | |
| { | |
| "event": "trained", | |
| "run": "run-20261022-141035-a3a720", | |
| "steps": 3200, | |
| "at": "2026-10-22T14:28:46+0200", | |
| "method": "from_scratch" | |
| }, | |
| { | |
| "event": "exported", | |
| "format": "gguf", | |
| "file": "convergent--pico-1.3-coding-large--1.3.1.f16.gguf", | |
| "at": "2026-10-22T14:28:48+0200" | |
| } | |
| ], | |
| "method": "from_scratch", | |
| "base_model": null, | |
| "license": "" | |
| } | |
| } |