Instructions to use Vikhrmodels/llama_asr_tts_35000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vikhrmodels/llama_asr_tts_35000 with Transformers:

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

pipe = pipeline("text-generation", model="Vikhrmodels/llama_asr_tts_35000")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Vikhrmodels/llama_asr_tts_35000")
model = AutoModelForCausalLM.from_pretrained("Vikhrmodels/llama_asr_tts_35000", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Vikhrmodels/llama_asr_tts_35000 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vikhrmodels/llama_asr_tts_35000"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vikhrmodels/llama_asr_tts_35000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Vikhrmodels/llama_asr_tts_35000

SGLang

How to use Vikhrmodels/llama_asr_tts_35000 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 "Vikhrmodels/llama_asr_tts_35000" \
    --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": "Vikhrmodels/llama_asr_tts_35000",
		"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 "Vikhrmodels/llama_asr_tts_35000" \
        --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": "Vikhrmodels/llama_asr_tts_35000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Vikhrmodels/llama_asr_tts_35000 with Docker Model Runner:
```
docker model run hf.co/Vikhrmodels/llama_asr_tts_35000
```

SALT - Speech and Language Transformer

SALT is a multimodal model that extends pre-trained large language models (LLMs) by incorporating new audio tokens to handle both Text-to-Speech (TTS) and Automatic Speech Recognition (ASR) tasks. Our approach bridges the gap between text and audio modalities by expanding the model’s vocabulary with audio representations using SpeechTokenizer.

Unlike existing models that require full retraining or rely on adapters, SALT leverages pre-trained LLM knowledge while fine-tuning for speech-specific tasks.

After resolving training challenges through precision adjustments (e.g., tf32), SALT exhibits stable and effective performance across both TTS and ASR domains.

Downloads last month: 7

Safetensors

Model size

1B params

Tensor type

F32

Model tree for Vikhrmodels/llama_asr_tts_35000

Base model

TinyLlama/TinyLlama_v1.1

Finetuned

(54)

this model

Vikhrmodels
/

llama_asr_tts_35000

SALT - Speech and Language Transformer

Model tree for Vikhrmodels/llama_asr_tts_35000

Dataset used to train Vikhrmodels/llama_asr_tts_35000