What are the hardware requirements for XLAM V0.1 R?

XLAM V0.1 R requires approximately 93.6 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed XLAM V0.1 R and how large is it?

XLAM V0.1 R is developed by Salesforce, a model with 46.7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does XLAM V0.1 R perform on standard benchmarks?

XLAM V0.1 R has the following published scores: MMLU 69.95. Compare against reference models on this page or on the LLM Explorer leaderboards.

XLAM V0.1 R by Salesforce — VRAM 93.6GB, 32K context

Name: XLAM V0.1 R
Author: Salesforce

XLAM V0.1 R is an open-source language model by Salesforce. Features: 46.7b LLM, VRAM: 93.6GB, Context: 32K, License: cc-by-nc-4.0, MoE, Instruction-Based, LLM Explorer Score: 0.13, Arc: 67.6, HellaSwag: 84.6, MMLU: 70, GSM8K: 58.4.

Arxiv:2402.15506 Arxiv:2406.18518 Arxiv:2409.03215 Conversational Endpoints compatible Instruct Mixtral Moe Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Salesforce/xLAM-v0.1-r

XLAM V0.1 R Parameters and Internals

Model Type

Large Language Model, Action Model

Use Cases

Applications:

Chatbot, Assistive Agent, Task Automation

Primary Use Cases:

Agent Tasks, Scenarios requiring large action modeling

Considerations:

Temperature tuning advised for different applications; formatting instructions needed for specific tasks.

Additional Notes

Sample Python code provided for usage.

Training Details

Methodology:

Fine-tuned across a wide range of agent tasks and scenarios.

Input Output

Input Format:

JSON-formatted messages comprising 'role' and 'content'.

Accepted Modalities:

text

Output Format:

Generated text

Performance Tips:

Lower Temperature for deterministic outcomes; include formatting instructions for complex tasks.

LLM Name	XLAM V0.1 R
Repository 🤗	https://huggingface.co/Salesforce/xLAM-v0.1-r
Model Size	46.7b
Required VRAM	93.6 GB
Updated	2026-07-21
Maintainer	Salesforce
Model Type	mixtral
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-19 5.0 GB: 2-of-19 5.0 GB: 3-of-19 4.9 GB: 4-of-19 5.0 GB: 5-of-19 5.0 GB: 6-of-19 4.9 GB: 7-of-19 5.0 GB: 8-of-19 5.0 GB: 9-of-19 4.9 GB: 10-of-19 5.0 GB: 11-of-19 5.0 GB: 12-of-19 5.0 GB: 13-of-19 4.9 GB: 14-of-19 5.0 GB: 15-of-19 5.0 GB: 16-of-19 4.9 GB: 17-of-19 5.0 GB: 18-of-19 4.2 GB: 19-of-19
Model Architecture	MixtralForCausalLM
License	cc-by-nc-4.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.37.2
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the XLAM V0.1 R capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to XLAM V0.1 R

Best Alternatives	Context / RAM	Downloads	Likes
Mixtral 8x7B Instruct V0.1	32K / 93.6 GB	842893	4720
Mixtral 8x7B Instruct V0.1	32K / 93.6 GB	7	0
Mixtral 8x7B Instruct V0.1 FP8	32K / 47.1 GB	3383	0
Merge Mixtral Prometheus 8x7B	32K / 91.9 GB	12	2
...xtral 8x7B Yes Instruct LimaRP	32K / 93.5 GB	5	1
BagelMIsteryTour V2 8x7B	32K / 93.5 GB	94	17
Notux 8x7b V1	32K / 93.6 GB	43	165
...rkrautLM Mixtral 8x7B Instruct	32K / 93.6 GB	84	22
Mixtral 8x7B Instruct V0.1 FP8	32K / 47.1 GB	374	0
Sage Ft Mixtral 8x7b	32K / 90 GB	45	25

Note: green Score (e.g. "73.2") means that the model is better than Salesforce/xLAM-v0.1-r.