Text Generation
Transformers
Safetensors
gemma-3
continued-pretraining
sft
synthetic-data
alignment-research
full-parameter
staged-training
path-dependence
Instructions to use jbostock/scimt-dispatch-midtrained-sft-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jbostock/scimt-dispatch-midtrained-sft-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jbostock/scimt-dispatch-midtrained-sft-v1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jbostock/scimt-dispatch-midtrained-sft-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jbostock/scimt-dispatch-midtrained-sft-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jbostock/scimt-dispatch-midtrained-sft-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jbostock/scimt-dispatch-midtrained-sft-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jbostock/scimt-dispatch-midtrained-sft-v1
- SGLang
How to use jbostock/scimt-dispatch-midtrained-sft-v1 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 "jbostock/scimt-dispatch-midtrained-sft-v1" \ --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": "jbostock/scimt-dispatch-midtrained-sft-v1", "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 "jbostock/scimt-dispatch-midtrained-sft-v1" \ --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": "jbostock/scimt-dispatch-midtrained-sft-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jbostock/scimt-dispatch-midtrained-sft-v1 with Docker Model Runner:
docker model run hf.co/jbostock/scimt-dispatch-midtrained-sft-v1
Add midtraining/charter/checkpoint-30 from immutable source
Browse files- .gitattributes +1 -0
- midtraining/charter/checkpoint-30/config.json +125 -0
- midtraining/charter/checkpoint-30/generation_config.json +13 -0
- midtraining/charter/checkpoint-30/model.safetensors +3 -0
- midtraining/charter/checkpoint-30/tokenizer.json +3 -0
- midtraining/charter/checkpoint-30/tokenizer_config.json +26 -0
- midtraining/charter/checkpoint-30/tokens_state.json +1 -0
- midtraining/charter/checkpoint-30/trainer_state.json +454 -0
- midtraining/charter/checkpoint-30/training_args.bin +3 -0
.gitattributes
CHANGED
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@@ -36,3 +36,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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midtraining/coin/checkpoint-2/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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midtraining/coin/checkpoint-30/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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midtraining/charter/checkpoint-2/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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midtraining/coin/checkpoint-2/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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midtraining/coin/checkpoint-30/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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| 38 |
midtraining/charter/checkpoint-2/tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
| 39 |
+
midtraining/charter/checkpoint-30/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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midtraining/charter/checkpoint-30/config.json
ADDED
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| 1 |
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{
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| 2 |
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"architectures": [
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| 3 |
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"Gemma3ForConditionalGeneration"
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| 4 |
+
],
|
| 5 |
+
"boi_token_index": 255999,
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| 6 |
+
"bos_token_id": 2,
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| 7 |
+
"dtype": "bfloat16",
|
| 8 |
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"eoi_token_index": 256000,
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| 9 |
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"eos_token_id": 1,
|
| 10 |
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"image_token_index": 262144,
|
| 11 |
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"initializer_range": 0.02,
|
| 12 |
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"mm_tokens_per_image": 256,
|
| 13 |
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"model_type": "gemma3",
|
| 14 |
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"pad_token_id": 0,
|
| 15 |
+
"text_config": {
|
| 16 |
+
"_sliding_window_pattern": 6,
|
| 17 |
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"attention_bias": false,
|
| 18 |
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"attention_dropout": 0.0,
|
| 19 |
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"attn_logit_softcapping": null,
|
| 20 |
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"bos_token_id": 2,
|
| 21 |
+
"cache_implementation": "hybrid",
|
| 22 |
+
"dtype": "bfloat16",
|
| 23 |
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"eos_token_id": 1,
|
| 24 |
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"final_logit_softcapping": null,
|
| 25 |
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"head_dim": 256,
|
| 26 |
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"hidden_activation": "gelu_pytorch_tanh",
|
| 27 |
+
"hidden_size": 3840,
|
| 28 |
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"initializer_range": 0.02,
|
| 29 |
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"intermediate_size": 15360,
|
| 30 |
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"layer_types": [
|
| 31 |
+
"sliding_attention",
|
| 32 |
+
"sliding_attention",
|
| 33 |
+
"sliding_attention",
|
| 34 |
+
"sliding_attention",
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| 35 |
+
"sliding_attention",
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| 36 |
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"full_attention",
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| 37 |
+
"sliding_attention",
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| 38 |
+
"sliding_attention",
|
| 39 |
+
"sliding_attention",
|
| 40 |
+
"sliding_attention",
|
| 41 |
+
"sliding_attention",
|
| 42 |
+
"full_attention",
|
| 43 |
+
"sliding_attention",
|
| 44 |
+
"sliding_attention",
|
| 45 |
+
"sliding_attention",
|
| 46 |
+
"sliding_attention",
|
| 47 |
+
"sliding_attention",
|
| 48 |
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"full_attention",
|
| 49 |
+
"sliding_attention",
|
| 50 |
+
"sliding_attention",
|
| 51 |
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"sliding_attention",
|
| 52 |
+
"sliding_attention",
|
| 53 |
+
"sliding_attention",
|
| 54 |
+
"full_attention",
|
| 55 |
+
"sliding_attention",
|
| 56 |
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"sliding_attention",
|
| 57 |
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"sliding_attention",
|
| 58 |
+
"sliding_attention",
|
| 59 |
+
"sliding_attention",
|
| 60 |
+
"full_attention",
|
| 61 |
+
"sliding_attention",
|
| 62 |
+
"sliding_attention",
|
| 63 |
+
"sliding_attention",
|
| 64 |
+
"sliding_attention",
|
| 65 |
+
"sliding_attention",
|
| 66 |
+
"full_attention",
|
| 67 |
+
"sliding_attention",
|
| 68 |
+
"sliding_attention",
|
| 69 |
+
"sliding_attention",
|
| 70 |
+
"sliding_attention",
|
| 71 |
+
"sliding_attention",
|
| 72 |
+
"full_attention",
|
| 73 |
+
"sliding_attention",
|
| 74 |
+
"sliding_attention",
|
| 75 |
+
"sliding_attention",
|
| 76 |
+
"sliding_attention",
|
| 77 |
+
"sliding_attention",
|
| 78 |
+
"full_attention"
|
| 79 |
+
],
|
| 80 |
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"max_position_embeddings": 131072,
|
| 81 |
+
"model_type": "gemma3_text",
|
| 82 |
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"num_attention_heads": 16,
|
| 83 |
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"num_hidden_layers": 48,
|
| 84 |
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"num_key_value_heads": 8,
|
| 85 |
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"pad_token_id": 0,
|
| 86 |
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"query_pre_attn_scalar": 256,
|
| 87 |
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"rms_norm_eps": 1e-06,
|
| 88 |
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"rope_parameters": {
|
| 89 |
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"full_attention": {
|
| 90 |
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"factor": 8.0,
|
| 91 |
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"rope_theta": 1000000.0,
|
| 92 |
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"rope_type": "linear"
|
| 93 |
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},
|
| 94 |
+
"sliding_attention": {
|
| 95 |
+
"rope_theta": 10000.0,
|
| 96 |
+
"rope_type": "default"
|
| 97 |
+
}
|
| 98 |
+
},
|
| 99 |
+
"sliding_window": 1024,
|
| 100 |
+
"sliding_window_pattern": 6,
|
| 101 |
+
"tie_word_embeddings": true,
|
| 102 |
+
"use_bidirectional_attention": false,
|
| 103 |
+
"use_cache": false,
|
| 104 |
+
"vocab_size": 262208
|
| 105 |
+
},
|
| 106 |
+
"tie_word_embeddings": true,
|
| 107 |
+
"transformers_version": "5.9.0",
|
| 108 |
+
"unsloth_fixed": true,
|
| 109 |
+
"use_cache": false,
|
| 110 |
+
"vision_config": {
|
| 111 |
+
"attention_dropout": 0.0,
|
| 112 |
+
"dtype": "bfloat16",
|
| 113 |
+
"hidden_act": "gelu_pytorch_tanh",
|
| 114 |
+
"hidden_size": 1152,
|
| 115 |
+
"image_size": 896,
|
| 116 |
+
"intermediate_size": 4304,
|
| 117 |
+
"layer_norm_eps": 1e-06,
|
| 118 |
+
"model_type": "siglip_vision_model",
|
| 119 |
+
"num_attention_heads": 16,
|
| 120 |
+
"num_channels": 3,
|
| 121 |
+
"num_hidden_layers": 27,
|
| 122 |
+
"patch_size": 14,
|
| 123 |
+
"vision_use_head": false
|
| 124 |
+
}
|
| 125 |
+
}
|
midtraining/charter/checkpoint-30/generation_config.json
ADDED
|
@@ -0,0 +1,13 @@
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+
{
|
| 2 |
+
"bos_token_id": 2,
|
| 3 |
+
"cache_implementation": "hybrid",
|
| 4 |
+
"do_sample": true,
|
| 5 |
+
"eos_token_id": [
|
| 6 |
+
1,
|
| 7 |
+
106
|
| 8 |
+
],
|
| 9 |
+
"pad_token_id": 0,
|
| 10 |
+
"top_k": 64,
|
| 11 |
+
"top_p": 0.95,
|
| 12 |
+
"transformers_version": "5.9.0"
|
| 13 |
+
}
|
midtraining/charter/checkpoint-30/model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:99cf8bc166cd5d92004d1bfe23e5dae7ed08901d1eacc01b0f7b7ec0063004e5
|
| 3 |
+
size 26388552360
|
midtraining/charter/checkpoint-30/tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:daab2354f8a74e70d70b4d1f804939b68a8c9624dd06cb7858e52dd8970e9726
|
| 3 |
+
size 33384567
|
midtraining/charter/checkpoint-30/tokenizer_config.json
ADDED
|
@@ -0,0 +1,26 @@
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| 1 |
+
{
|
| 2 |
+
"backend": "tokenizers",
|
| 3 |
+
"boi_token": "<start_of_image>",
|
| 4 |
+
"bos_token": "<bos>",
|
| 5 |
+
"clean_up_tokenization_spaces": false,
|
| 6 |
+
"eoi_token": "<end_of_image>",
|
| 7 |
+
"eos_token": "<eos>",
|
| 8 |
+
"image_token": "<image_soft_token>",
|
| 9 |
+
"is_local": true,
|
| 10 |
+
"local_files_only": false,
|
| 11 |
+
"mask_token": "<mask>",
|
| 12 |
+
"model_max_length": 131072,
|
| 13 |
+
"model_specific_special_tokens": {
|
| 14 |
+
"boi_token": "<start_of_image>",
|
| 15 |
+
"eoi_token": "<end_of_image>",
|
| 16 |
+
"image_token": "<image_soft_token>"
|
| 17 |
+
},
|
| 18 |
+
"pad_token": "<pad>",
|
| 19 |
+
"padding_side": "left",
|
| 20 |
+
"processor_class": "Gemma3Processor",
|
| 21 |
+
"sp_model_kwargs": null,
|
| 22 |
+
"spaces_between_special_tokens": false,
|
| 23 |
+
"tokenizer_class": "GemmaTokenizer",
|
| 24 |
+
"unk_token": "<unk>",
|
| 25 |
+
"use_default_system_prompt": false
|
| 26 |
+
}
|
midtraining/charter/checkpoint-30/tokens_state.json
ADDED
|
@@ -0,0 +1 @@
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| 1 |
+
{"total": 7864320, "trainable": 7844850}
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midtraining/charter/checkpoint-30/trainer_state.json
ADDED
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@@ -0,0 +1,454 @@
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