Text Generation
Transformers
Safetensors
llama
Merge
mergekit
lazymergekit
TinyLlama/TinyLlama-1.1B-Chat-v1.0
cognitivecomputations/TinyDolphin-2.8.2-1.1b-laser
cognitivecomputations/TinyDolphin-2.8.1-1.1b
TinyLlama/TinyLlama-1.1B-intermediate-step-715k-1.5T
text-generation-inference
Instructions to use jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-merge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-merge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-merge")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-merge") model = AutoModelForCausalLM.from_pretrained("jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-merge", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-merge with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-merge" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-merge", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-merge
- SGLang
How to use jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-merge 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 "jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-merge" \ --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": "jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-merge", "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 "jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-merge" \ --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": "jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-merge", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-merge with Docker Model Runner:
docker model run hf.co/jtatman/Tiny-Llama-Llama-Dolphin-laser-1b-merge
Update special_tokens_map.json
Browse files- special_tokens_map.json +2 -39
special_tokens_map.json
CHANGED
|
@@ -1,41 +1,4 @@
|
|
| 1 |
{
|
| 2 |
-
"additional_special_tokens": [
|
| 3 |
-
{
|
| 4 |
-
"content": "<|im_start|>user",
|
| 5 |
-
"lstrip": false,
|
| 6 |
-
"normalized": false,
|
| 7 |
-
"rstrip": false,
|
| 8 |
-
"single_word": false
|
| 9 |
-
},
|
| 10 |
-
{
|
| 11 |
-
"content": "<|im_end|>",
|
| 12 |
-
"lstrip": false,
|
| 13 |
-
"normalized": false,
|
| 14 |
-
"rstrip": false,
|
| 15 |
-
"single_word": false
|
| 16 |
-
},
|
| 17 |
-
{
|
| 18 |
-
"content": "<|im_start|>assistant",
|
| 19 |
-
"lstrip": false,
|
| 20 |
-
"normalized": false,
|
| 21 |
-
"rstrip": false,
|
| 22 |
-
"single_word": false
|
| 23 |
-
},
|
| 24 |
-
{
|
| 25 |
-
"content": "<|endoftext|>",
|
| 26 |
-
"lstrip": false,
|
| 27 |
-
"normalized": false,
|
| 28 |
-
"rstrip": false,
|
| 29 |
-
"single_word": false
|
| 30 |
-
},
|
| 31 |
-
{
|
| 32 |
-
"content": "<|im_start|>",
|
| 33 |
-
"lstrip": false,
|
| 34 |
-
"normalized": false,
|
| 35 |
-
"rstrip": false,
|
| 36 |
-
"single_word": false
|
| 37 |
-
}
|
| 38 |
-
],
|
| 39 |
"bos_token": {
|
| 40 |
"content": "<s>",
|
| 41 |
"lstrip": false,
|
|
@@ -44,13 +7,13 @@
|
|
| 44 |
"single_word": false
|
| 45 |
},
|
| 46 |
"eos_token": {
|
| 47 |
-
"content": "<
|
| 48 |
"lstrip": false,
|
| 49 |
"normalized": false,
|
| 50 |
"rstrip": false,
|
| 51 |
"single_word": false
|
| 52 |
},
|
| 53 |
-
"pad_token": "<
|
| 54 |
"unk_token": {
|
| 55 |
"content": "<unk>",
|
| 56 |
"lstrip": false,
|
|
|
|
| 1 |
{
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2 |
"bos_token": {
|
| 3 |
"content": "<s>",
|
| 4 |
"lstrip": false,
|
|
|
|
| 7 |
"single_word": false
|
| 8 |
},
|
| 9 |
"eos_token": {
|
| 10 |
+
"content": "</s>",
|
| 11 |
"lstrip": false,
|
| 12 |
"normalized": false,
|
| 13 |
"rstrip": false,
|
| 14 |
"single_word": false
|
| 15 |
},
|
| 16 |
+
"pad_token": "</s>",
|
| 17 |
"unk_token": {
|
| 18 |
"content": "<unk>",
|
| 19 |
"lstrip": false,
|