Instructions to use ricdomolm/talkie-1930-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ricdomolm/talkie-1930-coder with Transformers:

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

pipe = pipeline("text-generation", model="ricdomolm/talkie-1930-coder", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ricdomolm/talkie-1930-coder", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ricdomolm/talkie-1930-coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ricdomolm/talkie-1930-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-1930-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ricdomolm/talkie-1930-coder

SGLang

How to use ricdomolm/talkie-1930-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-1930-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-1930-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-1930-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-1930-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ricdomolm/talkie-1930-coder with Docker Model Runner:
```
docker model run hf.co/ricdomolm/talkie-1930-coder
```

talkie-1930-coder / tokenizer_config.json

ricdomolm

upload ckpt-2000 (SWE-12h v2 lr=2e-5)

9879210 verified 6 months ago

Raw History Blame Contribute Delete

220 Bytes

{
  "bos_token": null,
  "clean_up_tokenization_spaces": false,
  "eos_token": "<|end|>",
  "model_max_length": 65536,
  "pad_token": "<|endoftext|>",
  "tokenizer_class": "PreTrainedTokenizerFast",
  "unk_token": null
}

	{
	"bos_token": null,
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|end\|>",
	"model_max_length": 65536,
	"pad_token": "<\|endoftext\|>",
	"tokenizer_class": "PreTrainedTokenizerFast",
	"unk_token": null
	}