Instructions to use meihant/AnonymousModel-mht with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use meihant/AnonymousModel-mht with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="meihant/AnonymousModel-mht")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("meihant/AnonymousModel-mht", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use meihant/AnonymousModel-mht with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "meihant/AnonymousModel-mht" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "meihant/AnonymousModel-mht", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/meihant/AnonymousModel-mht
- SGLang
How to use meihant/AnonymousModel-mht 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 "meihant/AnonymousModel-mht" \ --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": "meihant/AnonymousModel-mht", "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 "meihant/AnonymousModel-mht" \ --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": "meihant/AnonymousModel-mht", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use meihant/AnonymousModel-mht with Docker Model Runner:
docker model run hf.co/meihant/AnonymousModel-mht
Finalize verified datasets/FCP-20B
Browse files- datasets/FCP-20B/README.md +74 -4
- datasets/FCP-20B/manifest.json +579 -9
datasets/FCP-20B/README.md
CHANGED
|
@@ -3,9 +3,10 @@
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|
| 3 |
Artifacts for the SmolLM2-360M 20B comparison. Stored under `datasets/FCP-20B/` in `meihant/AnonymousModel-mht` (a model repository).
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| 4 |
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| 5 |
{
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| 6 |
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"records":
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-
"
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| 8 |
-
"
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| 9 |
"format": "Exact pretokenized training corpus: int32 ids/labels; int64 offsets/body/supervised. -100 labels are masked. pack_starts indexes pack_ids, whose entries are global source-row indices.",
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| 10 |
"source_shards": [
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| 11 |
{
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|
@@ -402,7 +403,76 @@ Artifacts for the SmolLM2-360M 20B comparison. Stored under `datasets/FCP-20B/`
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}
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| 403 |
],
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| 404 |
"packing": "BFD, candidate buffer 1000, context 4096",
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-
"global_batch": 128
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}
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| 407 |
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| 408 |
See manifest.json for artifact hashes and completeness. The original corpus is the shared nominal-20B FineWeb-Edu source. BFD uses 4096-token sequences and 1000-record candidate buffers.
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| 3 |
Artifacts for the SmolLM2-360M 20B comparison. Stored under `datasets/FCP-20B/` in `meihant/AnonymousModel-mht` (a model repository).
|
| 4 |
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| 5 |
{
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| 6 |
+
"records": 49131472,
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| 7 |
+
"available_encoded_records": 49946998,
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| 8 |
+
"available_body_tokens": 20331230566,
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| 9 |
+
"packs": 12179735,
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| 10 |
"format": "Exact pretokenized training corpus: int32 ids/labels; int64 offsets/body/supervised. -100 labels are masked. pack_starts indexes pack_ids, whose entries are global source-row indices.",
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"source_shards": [
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{
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}
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],
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"packing": "BFD, candidate buffer 1000, context 4096",
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+
"global_batch": 128,
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"source_token_definition": "SmolLM2-360M native tokenizer, text.strip(), no special tokens, no truncation",
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"actual_source_tokens": 20000000040,
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"target_source_tokens": 20000000000,
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"training_source_records": 49131472,
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"available_source_records": 49946998,
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"segments": [
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{
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"segment": 0,
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"end_row": 4898104,
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"source_tokens": 2000000031,
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"target_tokens": 2000000000
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},
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{
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"segment": 1,
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"end_row": 9802903,
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"source_tokens": 4000000388,
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"target_tokens": 4000000000
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},
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{
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"segment": 2,
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"end_row": 14714284,
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"source_tokens": 6000000190,
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"target_tokens": 6000000000
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},
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{
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"segment": 3,
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"end_row": 19637704,
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"source_tokens": 8000000397,
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"target_tokens": 8000000000
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},
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{
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"segment": 4,
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"end_row": 24567228,
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"source_tokens": 10000000091,
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"target_tokens": 10000000000
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},
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{
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"segment": 5,
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"end_row": 29489067,
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"source_tokens": 12000000382,
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"target_tokens": 12000000000
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},
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{
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"segment": 6,
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"end_row": 34384959,
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"source_tokens": 14000000003,
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"target_tokens": 14000000000
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},
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{
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"segment": 7,
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"end_row": 39291912,
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"source_tokens": 16000000433,
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"target_tokens": 16000000000
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},
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{
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"segment": 8,
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"end_row": 44208957,
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"source_tokens": 18000000424,
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"target_tokens": 18000000000
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},
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{
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"segment": 9,
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"end_row": 49131472,
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"source_tokens": 20000000040,
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"target_tokens": 20000000000
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}
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],
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"checkpoint_policy": "permanent at shared source segment end, plus rolling recovery checkpoints",
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"cost_policy": "full preprocessing cost plus cumulative training GPU hours; no proportional preprocessing discount"
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}
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| 477 |
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| 478 |
See manifest.json for artifact hashes and completeness. The original corpus is the shared nominal-20B FineWeb-Edu source. BFD uses 4096-token sequences and 1000-record candidate buffers.
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datasets/FCP-20B/manifest.json
CHANGED
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@@ -1,9 +1,10 @@
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| 1 |
{
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"method": "fcp",
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"kind": "dataset",
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"records":
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"
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"
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"format": "Exact pretokenized training corpus: int32 ids/labels; int64 offsets/body/supervised. -100 labels are masked. pack_starts indexes pack_ids, whose entries are global source-row indices.",
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"source_shards": [
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{
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],
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"packing": "BFD, candidate buffer 1000, context 4096",
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"global_batch": 128,
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"files": [
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{
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"path": "encoded/000/ids.bin",
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@@ -2853,14 +2923,19 @@
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"sha256": "d7336a40587d842087acd3b35052f74e46eea0ce4109d6c6cd73b99d55621d92"
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},
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{
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"path": "packing/pack_starts.bin",
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"bytes":
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"sha256": "
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{
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"path": "packing/
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"bytes":
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"sha256": "
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{
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"path": "tokenizer/merges.txt",
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@@ -2896,6 +2971,501 @@
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"path": "tokenizer/chat_template.jinja",
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"bytes": 391,
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"sha256": "d2771c9694cc22f4d983cbf2344551d4d0933156a927f673584a358e4aee0563"
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| 2899 |
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| 2900 |
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| 2901 |
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| 1 |
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