Instructions to use xlr8harder/talkie-1930-13b-yarn-32k-from4k-step1000-tf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xlr8harder/talkie-1930-13b-yarn-32k-from4k-step1000-tf with Transformers:

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

pipe = pipeline("text-generation", model="xlr8harder/talkie-1930-13b-yarn-32k-from4k-step1000-tf", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("xlr8harder/talkie-1930-13b-yarn-32k-from4k-step1000-tf", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xlr8harder/talkie-1930-13b-yarn-32k-from4k-step1000-tf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xlr8harder/talkie-1930-13b-yarn-32k-from4k-step1000-tf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xlr8harder/talkie-1930-13b-yarn-32k-from4k-step1000-tf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/xlr8harder/talkie-1930-13b-yarn-32k-from4k-step1000-tf

SGLang

How to use xlr8harder/talkie-1930-13b-yarn-32k-from4k-step1000-tf 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 "xlr8harder/talkie-1930-13b-yarn-32k-from4k-step1000-tf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xlr8harder/talkie-1930-13b-yarn-32k-from4k-step1000-tf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "xlr8harder/talkie-1930-13b-yarn-32k-from4k-step1000-tf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xlr8harder/talkie-1930-13b-yarn-32k-from4k-step1000-tf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use xlr8harder/talkie-1930-13b-yarn-32k-from4k-step1000-tf with Docker Model Runner:
```
docker model run hf.co/xlr8harder/talkie-1930-13b-yarn-32k-from4k-step1000-tf
```

talkie-1930-13b-yarn-32k-from4k-step1000-tf

File size: 1,052 Bytes

97b2907

{
  "architectures": [
    "TalkieForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "configuration_talkie.TalkieConfig",
    "AutoModel": "modeling_talkie.TalkieModel",
    "AutoModelForCausalLM": "modeling_talkie.TalkieForCausalLM"
  },
  "dtype": "bfloat16",
  "eos_token_id": 65535,
  "head_dim": 128,
  "hidden_size": 5120,
  "logit_scale": 1.0,
  "max_position_embeddings": 32768,
  "model_type": "talkie",
  "n_embd": 5120,
  "n_head": 40,
  "n_layer": 40,
  "num_attention_heads": 40,
  "num_hidden_layers": 40,
  "pad_token_id": 65535,
  "rope_base": 1000000,
  "rope_parameters": {
    "beta_fast": 32.0,
    "beta_slow": 1.0,
    "factor": 8.0,
    "original_max_position_embeddings": 4096,
    "rope_type": "yarn"
  },
  "rope_scaling": {
    "beta_fast": 32.0,
    "beta_slow": 1.0,
    "factor": 8.0,
    "original_max_position_embeddings": 4096,
    "rope_type": "yarn"
  },
  "style": "base",
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.57.6",
  "use_cache": true,
  "vocab_size": 65536
}