Instructions to use ricdomolm/talkie-web-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ricdomolm/talkie-web-coder with Transformers:

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

pipe = pipeline("text-generation", model="ricdomolm/talkie-web-coder", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ricdomolm/talkie-web-coder", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ricdomolm/talkie-web-coder with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ricdomolm/talkie-web-coder

SGLang

How to use ricdomolm/talkie-web-coder 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 "ricdomolm/talkie-web-coder" \
    --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": "ricdomolm/talkie-web-coder",
		"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 "ricdomolm/talkie-web-coder" \
        --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": "ricdomolm/talkie-web-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ricdomolm/talkie-web-coder with Docker Model Runner:
```
docker model run hf.co/ricdomolm/talkie-web-coder
```

ricdomolm commited on 19 days ago

Commit

3834424

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Update README.md

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@@ -9,6 +9,10 @@ tags:
 - sft
 library_name: transformers
 pipeline_tag: text-generation
 ---
 # talkie-web-coder
@@ -76,4 +80,4 @@ vllm serve ricdomolm/talkie-web-coder \
 [`ricdomolm/talkie-1930-coder`](https://huggingface.co/ricdomolm/talkie-1930-coder)
 — same recipe, same SFT data, but starting from a different base model.
-Reaches 4.48% ± 0.69 pp on the same eval (n=5).

 - sft
 library_name: transformers
 pipeline_tag: text-generation
+datasets:
+- ricdomolm/mini-coder-trajs-400k
+base_model:
+- talkie-lm/talkie-web-13b-base
 ---
 # talkie-web-coder
 [`ricdomolm/talkie-1930-coder`](https://huggingface.co/ricdomolm/talkie-1930-coder)
 — same recipe, same SFT data, but starting from a different base model.
+Reaches 4.48% ± 0.69 pp on the same eval (n=5).