Instructions to use eventvoid/backtalk-80m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eventvoid/backtalk-80m with Transformers:

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

pipe = pipeline("text-generation", model="eventvoid/backtalk-80m")
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("eventvoid/backtalk-80m")
model = AutoModelForCausalLM.from_pretrained("eventvoid/backtalk-80m", 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

vLLM

How to use eventvoid/backtalk-80m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eventvoid/backtalk-80m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eventvoid/backtalk-80m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/eventvoid/backtalk-80m

SGLang

How to use eventvoid/backtalk-80m 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 "eventvoid/backtalk-80m" \
    --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": "eventvoid/backtalk-80m",
		"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 "eventvoid/backtalk-80m" \
        --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": "eventvoid/backtalk-80m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use eventvoid/backtalk-80m with Docker Model Runner:
```
docker model run hf.co/eventvoid/backtalk-80m
```

backtalk-80m

File size: 591 Bytes

3d88344

{
  "architectures": [
    "LlamaForCausalLM"
  ],
  "model_type": "llama",
  "hidden_size": 512,
  "intermediate_size": 1408,
  "num_hidden_layers": 20,
  "num_attention_heads": 8,
  "num_key_value_heads": 2,
  "head_dim": 64,
  "max_position_embeddings": 2048,
  "rope_theta": 100000.0,
  "rope_scaling": null,
  "rms_norm_eps": 1e-05,
  "vocab_size": 49152,
  "tie_word_embeddings": true,
  "hidden_act": "silu",
  "attention_bias": false,
  "mlp_bias": false,
  "bos_token_id": 1,
  "eos_token_id": 5,
  "pad_token_id": 0,
  "torch_dtype": "float16",
  "transformers_version": "4.57.0"
}