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

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

Pico-1.3-Coding-Large / Modelfile

Convergent-ch

Upload Pico 1.3 Coding Large

c694b11 verified 7 days ago

Raw History Blame Contribute Delete

483 Bytes

# Pico 1.3 Coding Large - ~7M-parameter SLM by Convergent. Writes simple Python/HTML when the
# message contains the word codingPico. Chat format: <|role|>\ncontent<|end|>\n
FROM ./pico-1.3-coding-large.gguf

TEMPLATE """{{- range .Messages }}<|{{ .Role }}|>
{{ .Content }}<|end|>
{{ end }}<|assistant|>
"""

PARAMETER stop "<|end|>"
PARAMETER stop "<|eos|>"
PARAMETER stop "<|user|>"
PARAMETER temperature 0.2
PARAMETER top_k 40
PARAMETER repeat_penalty 1.05
PARAMETER num_ctx 1024

	# Pico 1.3 Coding Large - ~7M-parameter SLM by Convergent. Writes simple Python/HTML when the
	# message contains the word codingPico. Chat format: <\|role\|>\ncontent<\|end\|>\n
	FROM ./pico-1.3-coding-large.gguf

	TEMPLATE """{{- range .Messages }}<\|{{ .Role }}\|>
	{{ .Content }}<\|end\|>
	{{ end }}<\|assistant\|>
	"""

	PARAMETER stop "<\|end\|>"
	PARAMETER stop "<\|eos\|>"
	PARAMETER stop "<\|user\|>"
	PARAMETER temperature 0.2
	PARAMETER top_k 40
	PARAMETER repeat_penalty 1.05
	PARAMETER num_ctx 1024