Instructions to use nm-testing/tinyllama-one-shot-dynamic-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nm-testing/tinyllama-one-shot-dynamic-test with Transformers:

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

pipe = pipeline("text-generation", model="nm-testing/tinyllama-one-shot-dynamic-test")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nm-testing/tinyllama-one-shot-dynamic-test")
model = AutoModelForCausalLM.from_pretrained("nm-testing/tinyllama-one-shot-dynamic-test")
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

vLLM

How to use nm-testing/tinyllama-one-shot-dynamic-test with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/nm-testing/tinyllama-one-shot-dynamic-test

SGLang

How to use nm-testing/tinyllama-one-shot-dynamic-test 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 "nm-testing/tinyllama-one-shot-dynamic-test" \
    --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": "nm-testing/tinyllama-one-shot-dynamic-test",
		"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 "nm-testing/tinyllama-one-shot-dynamic-test" \
        --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": "nm-testing/tinyllama-one-shot-dynamic-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nm-testing/tinyllama-one-shot-dynamic-test with Docker Model Runner:
```
docker model run hf.co/nm-testing/tinyllama-one-shot-dynamic-test
```

sadkins65 commited on May 28, 2024

Commit

393d709

verified ·

1 Parent(s): 01d7326

Upload folder using huggingface_hub

Browse files

Files changed (6) hide show

config.json +6 -7
generation_config.json +1 -1
model.safetensors +2 -2
recipe.yaml +6 -12
tokenizer.json +1 -0
tokenizer_config.json +1 -0

config.json CHANGED Viewed

@@ -32,16 +32,15 @@
             "num_bits": 8,
             "observer": "minmax",
             "observer_kwargs": {},
-            "strategy": "tensor",
             "symmetric": true,
             "type": "int"
           }
         }
       },
       "format": "int-quantized",
-      "global_compression_ratio": 1.4051191328987915,
       "ignore": [
-        "model.layers.0.mlp.down_proj",
         "lm_head"
       ],
       "quant_method": "sparseml",
@@ -49,9 +48,9 @@
     },
     "sparsity_config": {
       "format": "dense",
-      "global_sparsity": 7.700428565876607,
       "registry_requires_subclass": false,
-      "sparsity_structure": "0:0"
     }
   },
   "eos_token_id": 2,
@@ -69,8 +68,8 @@
   "rope_scaling": null,
   "rope_theta": 10000.0,
   "tie_word_embeddings": false,
-  "torch_dtype": "float32",
-  "transformers_version": "4.39.3",
   "use_cache": true,
   "vocab_size": 32000
 }

             "num_bits": 8,
             "observer": "minmax",
             "observer_kwargs": {},
+            "strategy": "channel",
             "symmetric": true,
             "type": "int"
           }
         }
       },
       "format": "int-quantized",
+      "global_compression_ratio": 1.2390773684863086,
       "ignore": [
         "lm_head"
       ],
       "quant_method": "sparseml",
     },
     "sparsity_config": {
       "format": "dense",
+      "global_sparsity": 1.188444907528722,
       "registry_requires_subclass": false,
+      "sparsity_structure": "unstructured"
     }
   },
   "eos_token_id": 2,
   "rope_scaling": null,
   "rope_theta": 10000.0,
   "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.40.0",
   "use_cache": true,
   "vocab_size": 32000
 }

generation_config.json CHANGED Viewed

@@ -3,5 +3,5 @@
   "eos_token_id": 2,
   "max_length": 2048,
   "pad_token_id": 0,
-  "transformers_version": "4.39.3"
 }

   "eos_token_id": 2,
   "max_length": 2048,
   "pad_token_id": 0,
+  "transformers_version": "4.40.0"
 }

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
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+size 1232041608

recipe.yaml CHANGED Viewed

@@ -1,16 +1,10 @@
-test_stage:
   quant_modifiers:
-    vLLMQuantizationModifier:
-      ignore: [lm_head, model.layers.0.mlp.down_proj]
       config_groups:
         group_0:
-          weights: {num_bits: 8, type: int, symmetric: true, strategy: tensor}
-          input_activations: {num_bits: 8, type: int, symmetric: true, strategy: token, dynamic: true}
-          output_activations: null
           targets: [Linear]
-    SparseGPTModifier:
-      sparsity: 0.0
-      block_size: 128
-      sequential_update: false
-      quantize: true
-      targets: ['re:model.layers.\d+$']

+quant_stage:
   quant_modifiers:
+    GPTQModifier:
+      sequential_update: false
+      ignore: [lm_head]
       config_groups:
         group_0:
+          weights: {num_bits: 8, type: int, symmetric: true, strategy: channel}
+          input_activations: {num_bits: 8, type: int, symmetric: true, dynamic: true, strategy: token}
           targets: [Linear]

tokenizer.json CHANGED Viewed

@@ -134,6 +134,7 @@
     "end_of_word_suffix": null,
     "fuse_unk": true,
     "byte_fallback": true,
     "vocab": {
       "<unk>": 0,
       "<s>": 1,

     "end_of_word_suffix": null,
     "fuse_unk": true,
     "byte_fallback": true,
+    "ignore_merges": false,
     "vocab": {
       "<unk>": 0,
       "<s>": 1,

tokenizer_config.json CHANGED Viewed

@@ -28,6 +28,7 @@
     }
   },
   "bos_token": "<s>",
   "clean_up_tokenization_spaces": false,
   "eos_token": "</s>",
   "legacy": false,

     }
   },
   "bos_token": "<s>",
+  "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
   "clean_up_tokenization_spaces": false,
   "eos_token": "</s>",
   "legacy": false,