Instructions to use CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B with Transformers:

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

pipe = pipeline("text-generation", model="CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B

SGLang

How to use CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B 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 "CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B" \
    --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": "CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B",
		"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 "CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B" \
        --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": "CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B with Docker Model Runner:
```
docker model run hf.co/CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B
```

CodeIsAbstract commited on 11 days ago

Commit

f169e35

verified ·

1 Parent(s): befdd81

Model save

Browse files

Files changed (8) hide show

README.md +55 -0
config.json +38 -0
generation_config.json +7 -0
model.safetensors +3 -0
special_tokens_map.json +6 -0
tokenizer.json +0 -0
tokenizer_config.json +0 -0
training_args.bin +3 -0

README.md ADDED Viewed

	@@ -0,0 +1,55 @@

+---
+library_name: transformers
+base_model: CodeIsAbstract/HybridTimeScaleModel
+tags:
+- generated_from_trainer
+model-index:
+- name: HybridTimeScaleModel-Instruct-Tuned-0.1B
+  results: []
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# HybridTimeScaleModel-Instruct-Tuned-0.1B
+This model is a fine-tuned version of [CodeIsAbstract/HybridTimeScaleModel](https://huggingface.co/CodeIsAbstract/HybridTimeScaleModel) on an unknown dataset.
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 4
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 64
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.05
+- num_epochs: 1
+### Training results
+### Framework versions
+- Transformers 4.46.3
+- Pytorch 2.8.0
+- Datasets 2.20.0
+- Tokenizers 0.20.3

config.json ADDED Viewed

	@@ -0,0 +1,38 @@

+{
+  "_name_or_path": "CodeIsAbstract/HybridTimeScaleModel",
+  "architectures": [
+    "HybridFourierLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "model.HybridFourierConfig",
+    "AutoModelForCausalLM": "model.HybridFourierLM"
+  },
+  "bos_token_id": 1,
+  "dropout": 0.05,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "latent_dim": 768,
+  "layer_types": [
+    "linear",
+    "linear",
+    "linear",
+    "softmax",
+    "linear",
+    "linear",
+    "linear",
+    "softmax",
+    "linear",
+    "linear",
+    "linear",
+    "softmax"
+  ],
+  "model_type": "hybrid_fourier_lm",
+  "num_layers": 12,
+  "num_modes": 64,
+  "pad_token_id": 2,
+  "time_scale": 128.0,
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.3",
+  "use_cache": false,
+  "vocab_size": 32768
+}

generation_config.json ADDED Viewed

	@@ -0,0 +1,7 @@

+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 2,
+  "transformers_version": "4.46.3"
+}

model.safetensors ADDED Viewed

	@@ -0,0 +1,3 @@

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+oid sha256:fe41cdda57a299db1c80ba95e498c2b11443ff1512c5d5a8f8ab8a33fe337d0a
+size 469337272

special_tokens_map.json ADDED Viewed

	@@ -0,0 +1,6 @@

+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "pad_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer.json ADDED Viewed

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tokenizer_config.json ADDED Viewed

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training_args.bin ADDED Viewed

	@@ -0,0 +1,3 @@

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+size 5777